GenAI Data Science Engineer

Role: AI/ML Engineer (Generative AI & Agentic Systems Focus)

We are seeking a highly skilled and hands-on AI/ML Engineer with deep practical experience in building, troubleshooting, and deploying Generative AI models and sophisticated AI agentic systems. The ideal candidate will not only understand the theory but can demonstrate significant real-world application and problem-solving.

Key Technical Requirements & Experience (Must-Haves):

1. Understanding of AI Agents: Candidates must demonstrate a clear understanding of AI agent concepts, including their core components and capabilities, and be able to differentiate them from more basic AI applications.

2. RAG Implementation & Troubleshooting: Proven experience in building and refining Retrieval Augmented Generation (RAG) systems. Candidates should be able to describe specific challenges encountered and how they were resolved.

3. Agent Tool Calling / Function Calling: Demonstrable experience implementing AI agents that utilize tool/function calls. Candidates should be able to discuss challenges and solutions related to tool integration.

4. Experience with Multiple LLMs: Practical experience working with various Large Language Models (e.g., GPT series, Llama, Claude, Gemini, open-source models) and an ability to discuss their relative merits for different tasks.

5. Proficiency in Agent Building Libraries/Frameworks: Significant hands-on experience using one or more established libraries/frameworks (e.g., Langchain, Langgraph, LlamaIndex, AutoGen, CrewAI, Microsoft Semantic Kernel) for agent development.

6. Agent Orchestration Skills (Highly Desirable): Experience in designing and implementing multi-agent systems or complex, multi-step agentic workflows. Candidates should be able to describe techniques for managing agent interactions and state.

7. LLM Output Control & Hallucination Mitigation: Proven ability to implement techniques for managing LLM output randomness and mitigating hallucinations, especially within RAG and agentic systems.

8. Testing & Evaluation of AI Agents: Experience in designing and implementing test cases and evaluation strategies specifically for AI agents.

9. Deployment of GenAI/Agentic Systems: Practical experience deploying GenAI applications and/or AI agents into production or production-like environments using modern MLOps practices (including containerization, CI/CD, and cloud platforms).

Highly Desirable Skills / Experience:

1. Competitive Coding / Algorithmic Problem Solving: Demonstrated proficiency in competitive coding platforms (e.g., LeetCode, HackerRank, TopCoder, Codeforces) or a strong, demonstrable foundation in algorithms and data structures, showcasing exceptional problem-solving abilities.

Candidate Profile:

1. Strong problem-solving and engineering mindset.

2. Excellent communication skills for conveying complex technical information.

3. Adaptable and enthusiastic about working in a fast-paced, R&D-driven environment.

4. Proactive in learning new AI technologies and methodologies.