Principal AI Architect (GenAI and Recommender Systems)

Role & responsibilities:

We are seeking GenAI and Agentic AI Engineering Hands-on Leader with a focus on delivery, client excellence and innovation. Experienced Senior AI Engineering Architect with deep expertise in AI recommendation and audience intelligence solutions using GenAI, embeddings, NLP, and predictive analyticss to design and implement enterprise-grade AI solutions. The ideal candidate combines strong technical leadership with hands-on experience architecting end-to-end AI/ML systemsfrom data readiness pipeline through Agentic Solutions deployment leveraging cloud-native architecture.

Test Driven Agentic AI Engineering, evaluation strategy, metric selection, ground-truth creation, and decisioning on model and prompting approaches. Youll build and validate GenAI/agentic solutions, define what good means, and ensure solutions are measurably effective and safe before and after launch. You will build the GenAI MVP solution in a production-intent way (model choice, RAG/agent behaviour, prompts, and evaluation).

What You’ll Work On:

• Design and develop enterprise-scale AI recommendation and audience intelligence solutions using GenAI, embeddings, NLP, and predictive analytics to drive business decision-making.
• Build and optimize AI-powered recommendation systems leveraging customer behavior data, similarity modeling, vector embeddings, and real-time personalization techniques.
• Develop scalable Conversational AI, LLM, and RAG-based solutions for intelligent insights, campaign optimization, and next-best-action recommendations.
• Architect and deploy production-grade AI/ML pipelines on cloud platforms (AWS/Azure/GCP) with strong focus on scalability, low-latency inference, MLOps, and continuous model improvement.
• Collaborate with cross-functional business, analytics, and engineering teams to translate complex business challenges into measurable AI-driven outcomes with enterprise-wide impact.
• Translate business needs into testable GenAI and Agentic Engineering solutions, clear outputs, and measurable success criteria; define scope boundaries (what the system should not attempt), including risks.
• Run feasibility assessments to choose the right approach: prompting vs RAG vs fine-tuning vs classical ML.
• Select and develop models based on task requirements (reasoning vs extraction vs classification) working with AI Engineering to understand latency/cost, and risk profile.
• Design prompting strategies: instruction design, few-shot sets, structured outputs, tool/agent prompts, and robustness patterns. This will be implemented as an MVP and iterate based on eval results.
• Establish prompt iteration methodology driven by evals (not anecdotal testing): prompt versioning, ablations, and change control.
• Define the evaluation plan for GenAI systems and agentic workflows- designing and implementing evaluation from LLM as a judge and ensure evaluation includes fairness and bias considerations where applicable. Define acceptance thresholds and release gates tied to these metrics.
• Own experimentation and model improvements: Run structured experiments (across prompts, retrievers, chunking, models).
• Develop out methods for identifying model failures such as hallucination types, retrieval misses, instruction-following errors, formatting failures etc.
• Provide recommendations for improvements grounded in evidence: what to change, expected lift, and trade-offs.
• Deliver an engineering-ready handoff: prompt packages and versioning approach, RAG configuration, tool schemas (if agentic), evaluation harness, datasets/ground truth, metric definitions, and go/no-go gates.

Preferred candidate profile

Technical Skills Required:

• Strong background in applied ML, data science, LLM and Agentic AI Engineering Systems with atleast10 years of experience.
• Deep expertise in evaluation design, metrics, and dataset curation for LLM systems.
• Proven experience in model selection and prompt engineering, including structured output and tool-use prompting.
• Strong proficiency in Python and major ML frameworks (PyTorch, TensorFlow, Scikit-learn).
• Strong experience in LLM fine-tuning, RAG Context Engineering, Claude Code, Open AI Codex, Agentic Workflows.
• Strong RAG design choices (chunking, embeddings, retrieval strategies, reranking) and how to evaluate them.
• Working with GenAI on Azure, AWS, or Snowflake involves leveraging cloud-native AI tools—such as Azure OpenAI, AWS Bedro…