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Position: Data Scientist AI/ML (DT80SI RM 3814)
Interview Drive on 19th Dec
Mandatory Skills: AI/ML Data Engineer Building AI Driven system- Primary- AI/ML
Job Description
At least 5+ years of experience ML Engineering/MLOps/LLMOps (must)
Strong foundation in LLM operations — prompt engineering, fine-tuning, and retrieval-augmented generation (RAG).
Hands-on experience with frameworks like LangChain, LlamaIndex, Hugging Face, or similar orchestration tools.
Proven ability to design and deploy on Azure Cloud (experience with AWS or GCP also valuable).
Relevant in Python- 5 years
What You’ll Do
Architect and deliver end-to-end AI solutions, from model design and RAG pipelines to scalable APIs and microservices.
Build and deploy agentic AI systems that enable autonomous task orchestration and reasoning.
Lead efforts to operationalize LLMs, including safety guardrails, evaluation pipelines, and performance monitoring.
Collaborate with product, data, and cloud teams to integrate AI capabilities into production environments.
Mentor engineers and set technical direction for AI development standards and best practices.
What You’ll Bring
10+ years of software engineering experience, including deep expertise in Python and production-grade ML/AI systems.
Atleast 5+ years of experience ML Engineering/MLOps/LLMOps (must)
Strong foundation in LLM operations — prompt engineering, fine-tuning, and retrieval-augmented generation (RAG).
Hands-on experience with frameworks like LangChain, LlamaIndex, Hugging Face, or similar orchestration tools.
Proven ability to design and deploy on Azure Cloud (experience with AWS or GCP also valuable).
Solid understanding of DevOps principles — CI/CD, containerization (Docker), and GitOps workflows.
Experience delivering reliable, well-tested, and maintainable code in agile environments.
Excellent communication skills and a collaborative, problem-solving mindset.
Nice to Have
Familiarity with Databricks and/or Snowflake.
Understanding of enterprise-grade security (OAuth, OIDC, SSO).
Experience with TDD, observability tools (e.g., New Relic, Splunk), and structured documentation (OpenAPI).
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