Data Scientist – Generative AI & Agentic AI

About the Role

We are seeking a Data Scientist specialized in Generative AI and Agentic AI to conceive, develop, and implement intelligent AI solutions tailored to address intricate business challenges. The candidate will engage extensively with product, engineering, and business units to translate requirements into scalable AI models and systems capable of complex reasoning, multi-step workflows, and tool utilization.

Primary Duties

• Design, train, assess, and deploy machine learning and deep learning frameworks aligned with business objectives.
• Develop production-ready Generative AI applications leveraging large language models and foundational AI models.
• Create Retrieval-Augmented Generation (RAG) applications encompassing embeddings, vector search databases, document processing, contextual retrieval, and reranking techniques.
• Architect and implement Agentic AI frameworks capable of planning, reasoning, task execution, multi-step decisions, and tool integration.
• Build AI agents utilizing frameworks such as LangChain, LangGraph, LlamaIndex, AutoGen, or comparable technologies.
• Integrate LLMs with external APIs, databases, enterprise software, search platforms, and internal utilities.
• Apply prompt engineering, structured output generation, function or tool invocations, memory and context management, and agent orchestration strategies.
• Fine-tune or adapt LLMs using advanced methods like LoRA/PEFT, instruction tuning, and supervised fine-tuning where applicable.
• Develop NLP and ML methodologies for text categorization, information extraction, semantic search, recommendation systems, forecasting, and anomaly identification.
• Design controlled experiments and evaluate models with relevant offline and online metrics.
• Implement evaluation frameworks for Generative AI measuring accuracy, relevance, groundedness, hallucination risk, response latency, and cost-efficiency.
• Conduct data preprocessing, feature engineering, exploratory and statistical analysis, and perform optimal model selection.
• Design scalable ML pipelines for ingestion, training, evaluation, deployment, and monitoring stages.
• Optimize LLM-based applications for high performance, reliability, low latency, scalability, and cost-effectiveness during inference.
• Enforce responsible AI principles including security, privacy, hallucination mitigation, prompt-injection defenses, and governance guardrails.
• Collaborate with software engineers for deploying AI solutions via REST APIs, microservices, and cloud infrastructures.
• Oversee production AI models and continuously improve their operational performance.
• Effectively communicate findings and AI insights to both technical and non-technical stakeholders.

Required Qualifications and Skills

Data Science and Machine Learning: Proficiency in Python and SQL with solid understanding of statistics and probability. Experience working with libraries like Scikit-learn, Pandas, NumPy, XGBoost, or LightGBM. Skilled in supervised and unsupervised learning, model evaluation, experimentation, and feature optimization.

Deep Learning and NLP: Practical experience with deep learning frameworks such as PyTorch or TensorFlow. Expertise in NLP concepts including transformers, embeddings, attention mechanisms, and architectures like BERT, T5, Llama, Mistral, or similar.

Generative AI: Hands-on development of applications using generative AI and LLMs. Strong command of prompt engineering, retrieval-augmented generation, vector embeddings, vector databases, semantic search, structured generation, function/tool calling, hallucination mitigation, and LLM evaluation methodologies. Experience interfacing with APIs or SDKs provided by leading LLM providers or open-source equivalents.

Agentic AI: Proven track record building AI agents and orchestrating multi-step agent workflows including planning, reasoning, tool calls, memory management, multi-agent coordination, human-in-the-loop implementation, agent performance evaluation, and observability. Familiarity with frameworks such as LangGraph, LangChain, LlamaIndex, AutoGen, or CrewAI is an advantage.

Cloud and MLOps: Experience deploying ML/AI models on AWS, Azure, or GCP platforms. Knowledge of Docker, Git, CI/CD pipelines, and RESTful APIs. Familiarity with ML lifecycle tools like MLflow or Kubeflow and expertise in monitoring, logging, model versioning, and troubleshooting production systems.

Data Engineering and Databases: Strong SQL proficiency and experience with relational databases including PostgreSQL, MySQL, or MongoDB. Familiarity with vector database technologies such as FAISS, Pinecone, Weaviate, Qdrant, or Chroma. Understanding of large-scale data pipelines and processing.

Eligibility

Applications are welcome from candidates holding any graduate degree.