Principal AI Engineer

Lead Data Scientist (GenAI, Agentic Systems & Applied Research)

Job Description

We are seeking a skilled Generative AI Developer with a strong data science and AI/ML foundation, who is actively working in or transitioning into Generative AI and Agent-based systems. The ideal candidate should have hands-on experience with Large Language Models (LLMs), prompt engineering, and modern GenAI solution design, along with a strong inclination toward building Proof-of-Concepts (PoCs) for agent-driven workflows.

This role is well-suited for a data scientist or applied AI engineer who enjoys experimentation, rapid prototyping, and translating research ideas into working GenAI solutions. You will work closely with architects and platform teams to design, build, and evaluate agentic GenAI systems using cloud-native platforms such as AWS Bedrock.

Key Responsibilities

• Design, develop, and maintain Generative AI solutions using LLMs, RAG pipelines, and agent-based architecture.
• Build and evaluate Proof-of-Concepts (PoCs) for autonomous and semi-autonomous agents, including task planning, tool usage, and multi-step reasoning.
• Experiment with agent orchestration patterns such as planner–executor, router–worker, and reflection-based agents.
• Integrate LLMs into enterprise applications using APIs and cloud services such as AWS Bedrock.
• Apply data science principles (analysis, evaluation metrics, experimentation) to assess GenAI and agent performance.
• Contribute to LLMOps practices, including prompt versioning, prompt testing, offline evaluations, and basic observability.
• Ability to manage cross-functional teams including Data Scientists, ML Engineers, Data Engineers, and business stakeholders.
• Continuously research emerging GenAI and agent frameworks and translate them into practical implementations.

Technical Requirements

• 8–10 years of total experience with 5+ years of hands-on experience in AI/ML, NLP, or Data Science.
• Strong background as a Data Scientist or Applied AI Engineer, with hands-on Python-based modeling and experimentation.
• Hands-on experience with LLMs (e.g., GPT, Claude, LLaMA, Mistral).
• Solid understanding of Retrieval-Augmented Generation (RAG) and vector databases (e.g., FAISS, Pinecone, OpenSearch).
• Practical experience building agent PoCs using frameworks such as:
  • • • • Crewai
        • LangGraph (or) custom agent orchestrators
• Experience designing agent workflows involving tool calling, memory, planning, and reflection loops.
• Familiarity with AWS Bedrock and LLM orchestration tools .
• Knowledge of LLMOps fundamentals: prompt versioning, evaluation, logging, and monitoring.
• Strong prompt engineering skills, including:
• Few-shot and structured prompting
• Hallucination control
• Schema-driven and JSON-based outputs
• Proficient in Python with experience building APIs and services.
• Good understanding of Deep Learning architectures (RNNs, CNNs, Transformers).
• Hands-on experience with NLP libraries such as spaCy, Hugging Face, and NLTK.
• Experience working with databases and search systems (PostgreSQL, Elasticsearch, OpenSearch).
• Experience using Streamlit (or similar) to build quick GenAI demos and PoC frontends.

Preferred Skills

• Experience with document processing, unstructured data extraction, and multimodal inputs (PDFs, images, PPTs).
• Exposure to MLOps / CI-CD pipelines (Docker, GitHub Actions, model evaluation workflows).
• Experience with agent evaluation techniques, such as step-level accuracy, tool-call validation, and cost-performance tradeoffs.
• Prior experience deploying GenAI solutions in production or near-production environments.
• Familiarity with cost optimization strategies for LLM and agent-based systems.

Soft Skills

• Strong analytical mindset with the ability to reason about model behavior and agent decisions.
• Curiosity-driven approach to research, experimentation, and PoC development.
• Ability to clearly communicate technical trade-offs and experimental findings.
• Collaborative mindset for working in cross-functional, fast-moving teams.