Senior Data Scientist

Generative AI Data Scientist / AI Engineer

Overview:

AI Engineer to help us build advanced solutions using Large Language Models (LLMs) and generative AI. If you love taking ownership, solving complex problems, and staying ahead of the curve in AI innovation, you’ll thrive here. You’ll work across the full development lifecycle designing, coding, testing, and deploying while collaborating with cross-functional teams to bring intelligent systems to life.

Key Responsibilities

• Design, develop, and deploy generative AI and agentic AI systems that autonomously execute multi-step tasks, adapt to changing contexts, and deliver intelligent outcomes.
• Conduct ongoing research to stay current with emerging trends and breakthroughs in generative AI and LLM technologies.
• Architect and implement AI agents (e.g., using LangGraph, AutoGen, CrewAI, or custom-built frameworks) capable of tool-calling, decision-making, memory retention, and recursive problem-solving.
• Develop robust pipelines that incorporate retrieval-augmented generation (RAG), vector search, and structured reasoning workflows.
• Evaluate agent performance using task success metrics, hallucination detection, grounding accuracy, and human-in-the-loop feedback systems.
• Stay on top of the latest developments in autonomous agents, multi-agent systems, and chain-of-thought/tree-of-thought prompting strategies.
• Demonstrate strong communication and teamwork skills, fostering a collaborative and productive work environment
• Contribute across the full software development lifecycle: design, coding, testing, deployment, and maintenance
• Participate in code reviews to ensure adherence to high standards of code quality and best practices
• Build and fine-tune large language models (LLMs) and integrate them with tool-use frameworks, planning modules, and knowledge bases.
• Build and maintain robust code libraries, tools, and frameworks to accelerate generative AI development

Technical Skills:

• Experience developing agent-based architectures that combine LLMs with APIs, databases, planning algorithms, and user interfaces.
• LLM libraries: LangChain, LlamaIndex, Hugging Face Transformers
• Agentic frameworks: LangGraph, AutoGen, CrewAI, Semantic Kernel
• Vector stores: Pinecone, Weaviate, FAISS, Chroma
• Model APIs: OpenAI, Anthropic, Mistral, Meta, etc.
• Deep understanding of prompt engineering, memory design, tool-calling APIs, and AI reasoning methods.
• Knowledge of responsible AI practices, including safety, bias mitigation, and user alignment in autonomous systems.
• Experience deploying autonomous agents in real-world applications (e.g., AI copilots, data automation, intelligent assistants).
• Familiarity with multi-agent coordination, agent memory management, and task decomposition techniques.
• Experience with open-source agent frameworks or custom-built orchestration layers.
• Understanding of MLOps practices and managing AI infrastructure at scale (e.g., Kubernetes, Ray, serverless functions).
• Published work or open-source contributions in LLM-based agents, planning, or multi-modal generative systems.