Tata Consultancy Services
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Overview Role involves designing and operationalizing GenAI capabilities, including LLMs and RAG, with emphasis on guardrails, observability, and scalable AI solution architecture.
Responsibilities
Implement Guardrails and observability across RAG and LLM applications
Setup GenAI Ops workflows to continuously monitor inference latency, throughput, quality and safety metrics
Define, track, and analyze RAG guardrail metrics using LLMs as Judges and SMLs (e.g., attribution, grounding, prompt injection, tone, PII leakage)
Implement annotation, structured feedback loops, fine-tuning, and alignment methods to calibrate judge models
Use LangChain to orchestrate guardrail checks, manage prompt versioning and integrate judge model scoring workflows
Work with OpenShift to deploy, scale and monitor containerized GenAI services
Build observability dashboards and alerts (Grafana or equivalent) for AI reliability
Contribute to emerging evaluation and guardrails as autonomous AI workflows expand
Qualifications
Bachelor’s or Master’s degree in Data Science, Computer Science, MIS, related field, or equivalent experience
10+ years of experience designing and developing AI solution architecture to scale
Proven hands-on experience in GenAI Ops – operationalizing LLM and RAG applications in production
Strong hands-on experience with the LangChain framework
Experience specifically with the OpenAI API, chat completions, embeddings, etc.
Solid awareness of TensorRT and VLLM implementation
Strong proficiency in Python and data science libraries (NumPy, Pandas, scikit-learn, PyTorch/TensorFlow)
Proven experience applying guardrails and observability to LLM or RAG-powered applications
Experience with LLMs as Judges and SMLs for evaluation (attribution, adherence, bias, PII, etc.)
Hands-on experience with OpenShift (or Kubernetes) for containerized AI workloads
Experience measuring and optimizing inference latency
Salary 100,000-125,000 per annum
Benefits TCS Employee Benefits Summary includes: Discretionary Annual Incentive; Comprehensive Medical Coverage (Medical & Health, Dental & Vision, Disability Planning & Insurance, Pet Insurance Plans); Family Support (Maternal & Parental Leaves); Insurance Options (Auto & Home Insurance, Identity Theft Protection); Convenience & Professional Growth (Commuter Benefits & Certification & Training Reimbursement); Time Off (Vacation, Time Off, Sick Leave & Holidays); Legal & Financial Assistance (Legal Assistance, 401K Plan, Performance Bonus, College Fund, Student Loan Refinancing).
Seniority level
Mid-Senior level
Employment type
Full-time
Job function
Engineering and Information Technology
Industries
IT Services and IT Consulting
Charlotte, NC
#J-18808-Ljbffr
Overview Role involves designing and operationalizing GenAI capabilities, including LLMs and RAG, with emphasis on guardrails, observability, and scalable AI solution architecture.
Responsibilities
Implement Guardrails and observability across RAG and LLM applications
Setup GenAI Ops workflows to continuously monitor inference latency, throughput, quality and safety metrics
Define, track, and analyze RAG guardrail metrics using LLMs as Judges and SMLs (e.g., attribution, grounding, prompt injection, tone, PII leakage)
Implement annotation, structured feedback loops, fine-tuning, and alignment methods to calibrate judge models
Use LangChain to orchestrate guardrail checks, manage prompt versioning and integrate judge model scoring workflows
Work with OpenShift to deploy, scale and monitor containerized GenAI services
Build observability dashboards and alerts (Grafana or equivalent) for AI reliability
Contribute to emerging evaluation and guardrails as autonomous AI workflows expand
Qualifications
Bachelor’s or Master’s degree in Data Science, Computer Science, MIS, related field, or equivalent experience
10+ years of experience designing and developing AI solution architecture to scale
Proven hands-on experience in GenAI Ops – operationalizing LLM and RAG applications in production
Strong hands-on experience with the LangChain framework
Experience specifically with the OpenAI API, chat completions, embeddings, etc.
Solid awareness of TensorRT and VLLM implementation
Strong proficiency in Python and data science libraries (NumPy, Pandas, scikit-learn, PyTorch/TensorFlow)
Proven experience applying guardrails and observability to LLM or RAG-powered applications
Experience with LLMs as Judges and SMLs for evaluation (attribution, adherence, bias, PII, etc.)
Hands-on experience with OpenShift (or Kubernetes) for containerized AI workloads
Experience measuring and optimizing inference latency
Salary 100,000-125,000 per annum
Benefits TCS Employee Benefits Summary includes: Discretionary Annual Incentive; Comprehensive Medical Coverage (Medical & Health, Dental & Vision, Disability Planning & Insurance, Pet Insurance Plans); Family Support (Maternal & Parental Leaves); Insurance Options (Auto & Home Insurance, Identity Theft Protection); Convenience & Professional Growth (Commuter Benefits & Certification & Training Reimbursement); Time Off (Vacation, Time Off, Sick Leave & Holidays); Legal & Financial Assistance (Legal Assistance, 401K Plan, Performance Bonus, College Fund, Student Loan Refinancing).
Seniority level
Mid-Senior level
Employment type
Full-time
Job function
Engineering and Information Technology
Industries
IT Services and IT Consulting
Charlotte, NC
#J-18808-Ljbffr