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Tier-1 brand, metro locations, and mid-level ML specialization increase competitive applicant density.
Strong ML/GenAI focus is transferable but specialization in RAG, embeddings, and vector DBs limits fit.
Explicit 5+ years, generative-AI mandates, and strict tech stack requirements drive high shortlisting strictness.
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Design, develop, and support Python-based AI applications focusing on classic and generative AI to advance Research & Consulting Delivery strategies.
Implement methodologies to rapidly roll out data analysis capabilities and transform unstructured data into modeling-ready formats using ML, NLP, and Gen AI.
Collaborate within globally distributed Agile teams to build internal productivity applications, maintaining clean code, documentation, and project plans.
5+ years experience in classic AI techniques with at least 2.5 years in generative AI techniques.
Proven experience building applications for knowledge search and summarization including evaluation frameworks for GenAI performance and observability implementation.
Proficiency in Python libraries such as Pandas, Scikit-Learn, Numpy, Scipy and experience deploying applications on AWS and Azure including familiarity with AWS Bedrock, Azure AI, and Databricks services.
Work Experience Required: 5+ years; Notice Period: Not explicitly mentioned in the JD.
Experienced in operationalizing advanced ML models including NLP, BERT, Transformers, deep learning, and agentic AI frameworks such as RAG, embedding models, vector DBs, Langgraph, MCP.
Comfortable working in Agile/Scrum environments within globally distributed teams, with strong stakeholder collaboration skills.
Capable of rapid development cycles with focus on clean coding practices, technical communication to diverse audiences, and delivering projects on-time within budget.