Advanced AI & Data Science Specialist – Multi‑Agent Intelligence

Location: Bengaluru

Key Responsibilities

AI System & Agentic Workflows

- Design, build, and optimize agentic AI systems using modern agent frameworks (e.g., Lang Graph, Auto Gen, Haystack Agents, ReAct-style agents).
- Implement multi-agent collaboration patterns, task orchestration, memory architectures, tool integrations, and reasoning-enhancing strategies.
- Develop secure, robust, and scalable tool‑calling and function-execution flows for agents operating in dynamic environments.

RAG Architecture & LLM Applications

- Architect and deploy RAG pipelines, including:
- Document ingestion, text splitting, metadata extraction
- Vector database integration (e.g., Postgress)
- Query transformation, retrieval optimization, re-ranking, and grounding
- Evaluate and optimize RAG components for latency, accuracy, hallucination reduction, and domain specificity.
- Integrate RAG workflows with LLMs from OpenAI, Azure OpenAI, Anthropic, or open-source models.

Data Science & Machine Learning

- Build and maintain end‑to‑end ML pipelines including feature engineering, model training, hyperparameter tuning, and deployment.
- Conduct exploratory data analysis (EDA), statistical modeling, and experiment design.
- Implement MLOps practices for versioning, monitoring, and model governance.

Software Engineering & Deployment

- Build production-grade APIs and microservices for AI systems.
- Work with cloud platforms (Azure/AWS/GCP) to deploy scalable and cost-efficient AI infrastructure.
- Establish CI/CD workflows for model deployment, evaluations, and automated testing.

Required

Skills & Qualifications

Technical Expertise

- 8+ years of industry experience in AI/ML engineering, data science, or full‑stack AI solution architecture.
- Strong proficiency in Python, including libraries such as PyTorch, Tensor Flow, Lang Chain/Lang Graph, Llama Index, Hugging Face, and Scikit-learn.
- Deep understanding of LLMs, embeddings, vector search, and RAG best practices.

- Experience with agentic AI architectures, tool-calling, and autonomous agent design.
- Hands-on experience with cloud-based AI services (Azure AI, AWS Sage Maker, GCP Vertex AI).
- Strong data engineering fundamentals (Spark, SQL, ETL pipelines).
- Experience deploying AI systems in production using containers (Docker), Kubernetes, or serverless architectures.

Soft Skills

- Strong problem-solving and analytical skills.
- Excellent communication and documentation capabilities.
- Ability to collaborate with cross-functional teams (Product, Engineering, Data, Dev Ops).
- Ownership mindset with strong focus on delivering high-quality solutions.

Preferred (Nice-to-Have) Skills

- Experience with evaluation frameworks (e.g., RAGAS, Lang Smith, Deep Eval).
- Familiarity with knowledge graph augmentation for enterprise RAG.
- Exposure to multi-modal AI systems (vision, speech, structured data).
- Understanding of data privacy, compliance, and AI safety practices.