Roles & Responsibilities
- 3-6 yrs into Development and deployment AI/ML models for use cases such as prediction, recommendation, classification, and anomaly detection across business functions.
- Build agentic AI systems that can autonomously execute multi-step workflows (e.g., data ingestion, reasoning, decision-making, and action execution) using modern agent frameworks.
- Design, fine-tune, and evaluate LLM-based solutions for tasks like document understanding, question answering, summarization, and conversational assistants.
- Implement Retrieval-Augmented Generation (RAG) pipelines using vector databases to ground LLMs on domain-specific content and internal knowledge bases.
- Collaborate with product managers, domain experts, data engineers, and software developers to translate business requirements into robust AI solutions and APIs.
- Develop and optimize prompt engineering strategies, including structured prompts, tool-calling, and multi-agent coordination for reliable outcomes.
- Integrate AI models and services into existing platforms via REST/GraphQL APIs, microservices, and event-driven architectures.
- Apply MLOps best practices for experiment tracking, versioning, CI/CD, monitoring, and observability of models and AI agents in production.
- Ensure strong data governance, privacy, and security practices when working with sensitive or proprietary data.
- Stay current with advancements in AI/ML, LLMs, agentic AI, and related tools to continuously improve solution quality and performance.
Desired Skills:
AI/ML Core
- Solid understanding of supervised, unsupervised, and deep learning techniques; familiarity with reinforcement learning is a plus.
- Hands-on experience with common ML frameworks such as TensorFlow, PyTorch, Scikit-learn, or XGBoost.
- Experience working with structured and unstructured data, including feature engineering, model evaluation, and performance optimization.
LLMs, NLP & Agentic AI
- Experience fine-tuning and deploying LLMs (e.g., GPT-family models, Llama, Claude, Mistral or similar) for real-world applications.
- Familiarity with embeddings, vector stores, and RAG architectures to enable semantic search and context-aware responses.
- Practical experience with agentic AI frameworks (e.g., LangChain, LangGraph, LlamaIndex, AutoGPT, CrewAI, or similar) and workflow orchestration tools.
- Strong skills in prompt engineering, prompt evaluation, and designing robust interaction patterns for LLM-based systems.
Programming, Data & MLOps
- Strong proficiency in Python and its data ecosystem (NumPy, Pandas, SciPy), and experience with FastAPI or Django for backend/API development.
- Experience with SQL and NoSQL databases (e.g., PostgreSQL, MySQL, MongoDB) and working with ETL/data pipelines.
- Familiarity with big data and distributed processing (e.g., Spark) is a plus.
- Experience with containerization and orchestration (Docker, Kubernetes) for deploying AI workloads at scale.
- Exposure to cloud platforms (AWS, Azure, GCP) and their AI/ML services.
- Knowledge of MLOps tools and practices for building reliable, maintainable AI systems in production environments.
Good to Have
- Experience working in any specific domain (e.g., SaaS, analytics, finance, taxation, customer experience, compliance) where AI was embedded into core workflows.
Contributions to internal frameworks, open-source projects, or research related to LLMs, agentic AI, or scalable ML systems
