Overview

Experience Level: 4 to 7 Years

Responsibilities

• Hands-on Development: Write clean, modular, and highly optimized Python code. Build, train, fine-tune, and deploy statistical ML, Deep Learning, NLP, and Generative AI models.
• AI System Design & Representation: Design scalable, robust, and end-to-end AI architectures. You must be able to visually represent your system designs (using UML, block diagrams, or flowcharts) and clearly explain the reasoning behind your architectural choices and trade-offs.
• Generative AI & Agentic Systems: Build and optimize state-of-the-art Generative AI applications, advanced Retrieval-Augmented Generation (RAG) pipelines, and Agentic AI workflows.
• MLOps & Production Engineering: Set up and maintain production-grade MLOps pipelines including CI/CD, automated testing, model registry, monitoring, and retraining frameworks.
• Technical Leadership & Mentoring: Act as a technical anchor for the team. Guide and mentor junior engineers, perform rigorous code reviews, and champion software engineering best practices.
• Client Engagement & Reasoning: Lead technical discussions with clients. Clearly articulate complex technical concepts, updates, risks, and blockers to both technical and non-technical audiences. You must be able to justify your technical decisions with strong analytical reasoning.

Qualifications

• Work location: Mumbai, Bangalore & Trivandrum
• Strong Programming Foundations:
• Exceptional proficiency in Python, with a deep understanding of class-based, object-oriented, and modular coding standards.
• Strong proficiency in SQL for querying, processing, and analyzing complex, large-scale datasets.
• Comprehensive understanding of coding standards, Git-based version control, and CI/CD practices.
• Core ML & Deep Learning:
• Hands-on experience developing and deploying statistical ML models (regression, classification, clustering).
• Strong theoretical and practical understanding of Deep Learning architectures, particularly Transformers, CNNs, and RNNs.
• Generative AI & Agentic AI:
• Practical experience designing and deploying Generative AI solutions and LLM-based applications.
• Hands-on implementation of advanced RAG (Retrieval-Augmented Generation) pipelines.
• Deep familiarity and hands-on experience with Vector Databases (e.g., Pinecone, Milvus, Chroma, Qdrant).
• Hands-on experience with Agentic AI Frameworks (e.g., LangChain, LlamaIndex, CrewAI, AutoGen) for multi-agent workflows and tool-use.
• AI System Design & Technical Reasoning:
• Proven ability to design scalable AI systems from scratch.
• Ability to visually diagram and represent architecture designs and explain technical trade-offs with deep, structured reasoning.
• Frameworks & Tools:
• Strong hands-on experience with PyTorch or TensorFlow.
• MLOps Basics:
• Good to have:
• Domain Expertise: Previous experience working in the Healthcare & Life Sciences domain (familiarity with HIPAA, clinical data standards, or healthcare compliance is a huge plus).
• Databricks & PySpark:
• Hands-on experience with PySpark for distributed data processing and large-scale feature engineering.
• Agile Methodologies: Experience working in Agile/Scrum environments.
• Technical Reasoning & Depth: Ability to explain complex technical decisions, architecture designs, and model choices under deep probing (explaining the why, not just the how).
• Visual Communication: Comfort in using visual tools to present and explain complex system integrations.
• Client-Facing Presence: A pleasant, charismatic, and articulate communication style. Ability to lead technical discussions with clients, address risks, and resolve blockers.
• Mentorship: Passion for guiding junior engineers and fostering a culture of continuous learning and high engineering standards.
• Cutting-Edge Stack: Work with the latest 2026 AI/ML innovations, including Agentic AI, LLMs, and advanced MLOps.
• End-to-End Ownership: Own your deliverables from initial concept and system architecture to production deployment.