Job Title: Data Scientist ML, GenAI & Agentic AI
Position Summary
We are seeking a Data Scientist with a strong background in enterprise-scale machine learning, deep expertise in LLMs and Generative AI, and a clear understanding of the evolving Agentic AI ecosystem. The ideal candidate has hands-on experience developing predictive models, recommendation systems, and LLM-powered solutions, and is passionate about leveraging cutting-edge AI to solve complex enterprise challenges.
This role will involve working closely with product, engineering, and business teams to design, build, and deploy impactful AI solutions that are both technically robust and business-aligned.
Key Responsibilities
ML & Predictive Systems Development
- Design, develop, and deploy enterprise-grade machine learning models
for recommendations, predictions, and personalization use cases.
- Work on problems such as churn prediction, intelligent routing, anomaly detection, and behavior modeling.
- Leverage techniques in supervised, unsupervised, and reinforcement learning as needed based on business context.
LLMs & Generative AI
- Build and fine-tune LLM-based solutions (e.g., GPT, LLaMA, Claude, or open-source models) for tasks such as summarization, semantic search, document understanding, and copilots.
- Deliver production-ready GenAI projects, applying techniques like RAG (Retrieval-Augmented Generation), prompt engineering, fine-tuning, and vector search (e.g., FAISS, Pinecone, Weaviate).
- Collaborate with engineering to embed LLM workflows into enterprise applications, ensuring scalability and performance.
Agentic AI & Ecosystem Engagement
- Contribute thought leadership and experimentation around Agentic AI architectures, task orchestration, memory management, tool integration, and decision autonomy.
- Stay ahead of trends in the open-source and commercial LLM/AI space, including LangChain, AutoGen, DSPy, and ADK-based systems.
- Develop internal PoCs or evaluate frameworks to assess viability for enterprise use.
Collaboration & Delivery
- Work with cross-functional teams to identify AI opportunities and define technical roadmaps.
- Translate business needs into data science problems, define success metrics, and communicate results to stakeholders.
- Ensure model governance, monitoring, and explainability for AI systems in production.
Required Qualifications
- Masters or PhD in Computer Science, Data Science, Statistics, or related field.
- 58 years of experience in data science and ML, with strong enterprise project delivery experience.
- Proven success in building and deploying ML models and recommendation systems at scale.
- 2+ projects delivered involving LLMs and Generative AI, with hands-on experience in one or more of: OpenAI, Hugging Face Transformers, LangChain, Vector DBs, or model fine-tuning.
- Advanced Python programming skills and experience with ML libraries (e.g., Scikit-learn, XGBoost, PyTorch, TensorFlow).
- Experience with cloud-based ML/AI platforms (e.g., Vertex AI, AWS SageMaker, Azure ML).
- Strong understanding of system architecture, APIs, data pipelines, and model integration patterns.
Preferred Qualifications
- Experience with Agentic AI frameworks and orchestration systems (LangChain, AutoGen, ADK, CrewAI).
- Familiarity with prompt optimization, tool chaining, task planning, and autonomous agents.
- Working knowledge of MLOps best practices including model versioning, CI/CD for ML, and model monitoring.
- Strong communication skills and ability to advocate for AI-driven solutions across technical and non-technical teams.
- Regular follower of AI research, open-source trends, and GenAI product developments.