OPT AI Data Specialist Jobs
AI Data Specialist roles involve labeling, curating, and evaluating datasets that train machine learning models, making them a strong fit for F-1 OPT students with backgrounds in computer science, data science, or linguistics. Many employers in this space have experience working with international candidates on STEM OPT, which covers the full 36-month authorization period.
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GenAI Engineer Applied AI Engineer LLM Agent Systems
Location
Onsite Charlotte NC
Addison TX
Jersey City NJ
Candidate must be willing to work onsite; remote is not an option.
Role Summary
We are seeking a highly skilled GenAI Engineer to design, build, and operationalize next-generation AI solutions leveraging Large Language Models (LLMs), AI agents, Retrieval-Augmented Generation (RAG) architectures, and scalable cloud platforms. This role requires strong hands-on expertise across AI concepts, model integration, data pipelines, and MLOps/CICD with the ability to translate business problems into production-grade AI systems.
Key Responsibilities
GenAI LLM Engineering:
- Design, develop, and deploy LLM-powered applications using leading foundation models (OpenAI, Azure OpenAI, Anthropic, open-source LLMs).
- Build LLM-based AI agents capable of multistep reasoning, tool use, orchestration, and autonomous workflows.
- Implement and optimize agent frameworks (LangChain, LlamaIndex, Semantic Kernel, AutoGen, CrewAI, etc.).
- Engineer robust prompting strategies, memory mechanisms, and tool-augmented reasoning.
RAG Knowledge Systems:
- Design and implement Retrieval-Augmented Generation (RAG) architectures.
- Build embedding pipelines using vector databases (FAISS, Pinecone, Weaviate, Azure AI Search, Chroma).
- Optimize document ingestion, chunking strategies, metadata management, and reranking.
- Ensure accuracy, relevance, and performance of AI-generated responses.
Machine Learning Model Integration:
- Apply practical ML concepts including classification, clustering, ranking, and similarity search where applicable.
- Integrate traditional ML models with LLM-based systems for hybrid AI solutions.
- Evaluate, fine-tune, and test models using appropriate performance metrics.
Data Engineering Pipelines:
- Develop and maintain data pipelines for structured and unstructured data using Python and SQL.
- Work with large datasets, APIs, and streaming/batch processing frameworks.
- Ensure data quality, lineage, observability, and governance within AI workflows.
MLOps CICD Productionization:
- Build CICD pipelines for AI and ML workloads including model versioning and automated testing.
- Deploy AI services in containerized environments (Docker, Kubernetes).
- Implement monitoring for model performance, drift, latency, and cost.
- Ensure security, access control, and compliance for AI systems.
Cloud Platform Engineering:
- Design and deploy AI solutions on cloud platforms such as AWS, Azure, or GCP.
- Leverage managed AIML services, serverless components, and scalable infrastructure.
- Optimize cost, performance, and reliability of AI workloads.
Collaboration Stakeholder Engagement:
- Partner with product, platform, and business teams to translate requirements into AI solutions.
- Document architectures, design decisions, and operational runbooks.
- Provide guidance on GenAI best practices, risks, and responsible AI usage.
Required Skills Experience
Core Technical Skills:
- Strong proficiency in Python and working knowledge of SQL.
- Solid foundation in AIML concepts with hands-on experience deploying models.
- Proven experience with LLMs, AI agents, and agent frameworks.
- Hands-on expertise with RAG architectures and vector databases.
- Experience implementing CICD pipelines for AI or ML systems.
- Strong understanding of data pipelines and distributed data processing.
- Experience working on at least one major cloud platform (AWS, Azure, or GCP).
Preferred Good to Have:
- Experience fine-tuning LLMs (LoRA, PEFT, RLHF concepts).
- Familiarity with evaluation frameworks for GenAI (hallucination testing, grounding, latency benchmarks).
- Exposure to governance, security, and compliance considerations for enterprise AI.
- Background in domains such as BFSI, healthcare, or regulated industries.
Education
Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related field or equivalent practical experience.
What Success Looks Like
- Scalable, reliable GenAI solutions deployed to production.
- Well-architected AI agents delivering measurable business value.
- High-quality, explainable, and maintainable AI systems.
- Strong collaboration across engineering, data, and business teams.
Skills
Mandatory Skills: GenAI - LLMOps, LLMOps
Benefits and Perks:
- Comprehensive Medical Plan Covering Medical, Dental, Vision
- Short Term and Long-Term Disability Coverage
- 401(k) Plan with Company match
- Life Insurance
- Vacation Time, Sick Leave, Paid Holidays
- Paid Paternity and Maternity Leave
The range displayed on each job posting reflects the minimum and maximum salary target for the position across all US locations. Within the range, individual pay is determined by work location and job level and additional factors including job-related skills, experience, and relevant education or training. Depending on the position offered, other forms of compensation may be provided as part of overall compensation like an annual performance-based bonus, sales incentive pay, and other forms of bonus or variable compensation.
Disclaimer: The compensation and benefits information provided herein is accurate as of the date of this posting.
LTIMindtree is an equal opportunity employer that is committed to diversity in the workplace. Our employment decisions are made without regard to race, color, creed, religion, sex (including pregnancy, childbirth or related medical conditions), gender identity or expression, national origin, ancestry, age, family-care status, veteran status, marital status, civil union status, domestic partnership status, military service, handicap or disability or history of handicap or disability, genetic information, atypical hereditary cellular or blood trait, union affiliation, affectional or sexual orientation or preference, or any other characteristic protected by applicable federal, state, or local law, except where such considerations are bona fide occupational qualifications permitted by law.
Benefits
Compensation range: $93,000.00 to $142,000.00 per year
About LTM
LTM is an AI-centric global technology services company and the Business Creativity partner to the world’s largest and most disruptive enterprises. We bring human insights and intelligent systems together to help clients create greater value at the intersection of technology and domain expertise. Our capabilities span integrated operations, transformation, and business AI — enabling new ways of working, new productivity paradigms, and new roads to value. Together with over 87,000 employees across 40 countries and our global network of partners, LTM — a Larsen & Toubro company — owns business outcomes for our clients, helping them not just outperform the market, but to Outcreate it.
Please also note that neither LTM nor any of its authorized recruitment agencies/partners charge any candidate registration fee or any other fees from talent (candidates) towards appearing for an interview or securing employment/internship. Candidates shall be solely responsible for verifying the credentials of any agency/consultant that claims to be working with LTM for recruitment. Please note that anyone who relies on the representations made by fraudulent employment agencies does so at their own risk, and LTM disclaims any liability in case of loss or damage suffered as a consequence of the same.
Job Title
GenAI Engineer Applied AI Engineer LLM Agent Systems
Location
Onsite Charlotte NC
Addison TX
Jersey City NJ
Candidate must be willing to work onsite; remote is not an option.
Role Summary
We are seeking a highly skilled GenAI Engineer to design, build, and operationalize next-generation AI solutions leveraging Large Language Models (LLMs), AI agents, Retrieval-Augmented Generation (RAG) architectures, and scalable cloud platforms. This role requires strong hands-on expertise across AI concepts, model integration, data pipelines, and MLOps/CICD with the ability to translate business problems into production-grade AI systems.
Key Responsibilities
GenAI LLM Engineering:
- Design, develop, and deploy LLM-powered applications using leading foundation models (OpenAI, Azure OpenAI, Anthropic, open-source LLMs).
- Build LLM-based AI agents capable of multistep reasoning, tool use, orchestration, and autonomous workflows.
- Implement and optimize agent frameworks (LangChain, LlamaIndex, Semantic Kernel, AutoGen, CrewAI, etc.).
- Engineer robust prompting strategies, memory mechanisms, and tool-augmented reasoning.
RAG Knowledge Systems:
- Design and implement Retrieval-Augmented Generation (RAG) architectures.
- Build embedding pipelines using vector databases (FAISS, Pinecone, Weaviate, Azure AI Search, Chroma).
- Optimize document ingestion, chunking strategies, metadata management, and reranking.
- Ensure accuracy, relevance, and performance of AI-generated responses.
Machine Learning Model Integration:
- Apply practical ML concepts including classification, clustering, ranking, and similarity search where applicable.
- Integrate traditional ML models with LLM-based systems for hybrid AI solutions.
- Evaluate, fine-tune, and test models using appropriate performance metrics.
Data Engineering Pipelines:
- Develop and maintain data pipelines for structured and unstructured data using Python and SQL.
- Work with large datasets, APIs, and streaming/batch processing frameworks.
- Ensure data quality, lineage, observability, and governance within AI workflows.
MLOps CICD Productionization:
- Build CICD pipelines for AI and ML workloads including model versioning and automated testing.
- Deploy AI services in containerized environments (Docker, Kubernetes).
- Implement monitoring for model performance, drift, latency, and cost.
- Ensure security, access control, and compliance for AI systems.
Cloud Platform Engineering:
- Design and deploy AI solutions on cloud platforms such as AWS, Azure, or GCP.
- Leverage managed AIML services, serverless components, and scalable infrastructure.
- Optimize cost, performance, and reliability of AI workloads.
Collaboration Stakeholder Engagement:
- Partner with product, platform, and business teams to translate requirements into AI solutions.
- Document architectures, design decisions, and operational runbooks.
- Provide guidance on GenAI best practices, risks, and responsible AI usage.
Required Skills Experience
Core Technical Skills:
- Strong proficiency in Python and working knowledge of SQL.
- Solid foundation in AIML concepts with hands-on experience deploying models.
- Proven experience with LLMs, AI agents, and agent frameworks.
- Hands-on expertise with RAG architectures and vector databases.
- Experience implementing CICD pipelines for AI or ML systems.
- Strong understanding of data pipelines and distributed data processing.
- Experience working on at least one major cloud platform (AWS, Azure, or GCP).
Preferred Good to Have:
- Experience fine-tuning LLMs (LoRA, PEFT, RLHF concepts).
- Familiarity with evaluation frameworks for GenAI (hallucination testing, grounding, latency benchmarks).
- Exposure to governance, security, and compliance considerations for enterprise AI.
- Background in domains such as BFSI, healthcare, or regulated industries.
Education
Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related field or equivalent practical experience.
What Success Looks Like
- Scalable, reliable GenAI solutions deployed to production.
- Well-architected AI agents delivering measurable business value.
- High-quality, explainable, and maintainable AI systems.
- Strong collaboration across engineering, data, and business teams.
Skills
Mandatory Skills: GenAI - LLMOps, LLMOps
Benefits and Perks:
- Comprehensive Medical Plan Covering Medical, Dental, Vision
- Short Term and Long-Term Disability Coverage
- 401(k) Plan with Company match
- Life Insurance
- Vacation Time, Sick Leave, Paid Holidays
- Paid Paternity and Maternity Leave
The range displayed on each job posting reflects the minimum and maximum salary target for the position across all US locations. Within the range, individual pay is determined by work location and job level and additional factors including job-related skills, experience, and relevant education or training. Depending on the position offered, other forms of compensation may be provided as part of overall compensation like an annual performance-based bonus, sales incentive pay, and other forms of bonus or variable compensation.
Disclaimer: The compensation and benefits information provided herein is accurate as of the date of this posting.
LTIMindtree is an equal opportunity employer that is committed to diversity in the workplace. Our employment decisions are made without regard to race, color, creed, religion, sex (including pregnancy, childbirth or related medical conditions), gender identity or expression, national origin, ancestry, age, family-care status, veteran status, marital status, civil union status, domestic partnership status, military service, handicap or disability or history of handicap or disability, genetic information, atypical hereditary cellular or blood trait, union affiliation, affectional or sexual orientation or preference, or any other characteristic protected by applicable federal, state, or local law, except where such considerations are bona fide occupational qualifications permitted by law.
Benefits
Compensation range: $93,000.00 to $142,000.00 per year
About LTM
LTM is an AI-centric global technology services company and the Business Creativity partner to the world’s largest and most disruptive enterprises. We bring human insights and intelligent systems together to help clients create greater value at the intersection of technology and domain expertise. Our capabilities span integrated operations, transformation, and business AI — enabling new ways of working, new productivity paradigms, and new roads to value. Together with over 87,000 employees across 40 countries and our global network of partners, LTM — a Larsen & Toubro company — owns business outcomes for our clients, helping them not just outperform the market, but to Outcreate it.
Please also note that neither LTM nor any of its authorized recruitment agencies/partners charge any candidate registration fee or any other fees from talent (candidates) towards appearing for an interview or securing employment/internship. Candidates shall be solely responsible for verifying the credentials of any agency/consultant that claims to be working with LTM for recruitment. Please note that anyone who relies on the representations made by fraudulent employment agencies does so at their own risk, and LTM disclaims any liability in case of loss or damage suffered as a consequence of the same.
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Get Access To All JobsTips for Finding OPT Sponsorship as an AI Data Specialist
Lead with your STEM OPT timeline upfront
Mention your 36-month STEM OPT authorization early in applications and interviews. Many hiring managers don't distinguish between standard and STEM OPT, so clarifying the timeline removes the biggest objection before it becomes one.
Target companies with active AI research pipelines
AI labs, autonomous vehicle companies, and enterprise software firms run continuous data annotation and evaluation cycles. These organizations hire OPT candidates more regularly because data work scales with headcount, not visa lottery timing.
Highlight domain-specific annotation experience
If you've labeled medical imaging, multilingual text, or audio datasets, call that out specifically. Domain expertise makes you harder to replace and signals to employers that sponsoring you after OPT is worth the investment.
Show familiarity with data quality frameworks
Employers want candidates who understand inter-annotator agreement, schema design, and quality control pipelines, not just labeling tools. Framing your experience around data quality positions you for senior roles that are more likely to sponsor.
Connect your degree field to the role directly
STEM OPT eligibility depends on your degree field. Linguistics, statistics, computer science, and cognitive science all qualify. Make that connection explicit on your resume so hiring managers and HR teams don't question your authorization status.
Apply before your OPT clock runs low
Employers are more comfortable starting H-1B visa or other sponsorship conversations with 18 or more months remaining on your OPT. Starting your job search early gives you negotiating room and time to find a company willing to sponsor.
AI Data Specialist OPT: Frequently Asked Questions
Do AI Data Specialist roles qualify for STEM OPT extension?
Yes, in most cases. AI Data Specialist positions typically fall under computer science, data science, or information technology degree classifications, all of which are on the STEM Designated Degree Program list. Your degree field, not the job title, determines eligibility, so confirm your CIP code with your DSO before applying.
How do I find AI Data Specialist jobs that are open to OPT candidates?
Migrate Mate filters job listings specifically for visa sponsorship openness, so you're not wasting applications on employers who won't consider international candidates. Searching there saves significant time compared to filtering through general listings and cold-emailing recruiters to ask about sponsorship policies.
Will I need H-1B sponsorship to stay in this role long-term?
For most full-time positions, yes. After STEM OPT ends, H-1B is the most common next step, though some AI data roles at qualifying nonprofits or universities may be cap-exempt. Start sponsorship conversations with your employer at least 12 months before your OPT expires to give adequate time for H-1B preparation.
Can I work as a contractor or on a 1099 basis as an AI Data Specialist on OPT?
OPT requires that your employment be directly related to your degree field, but self-employment and independent contracting are permitted as long as you're working for yourself, not classified as an employee of a single client. If a company wants to hire you as a contractor through their own payroll vendor, that typically qualifies. Consult your DSO to confirm any arrangement.
What should I include on my resume to pass OPT employer screening for this role?
List your visa status, OPT end date, and STEM extension eligibility clearly in a summary or header section. For AI Data Specialist roles specifically, include annotation tools you've used, dataset types you've worked with, and any quality assurance or inter-annotator agreement experience. Employers in AI move quickly and want to assess authorization at a glance.