Job Specifications
LEAD AI ENGINEER
Location: Bengaluru, Hybrid | Type: Full-time
About Newpage Solutions
Newpage Solutions is a global digital health innovation company helping people live longer, healthier lives. We partner with life sciences organisations which include, pharmaceutical, biotech and healthcare leaders, to build transformative AI and data driven technologies addressing real-world health challenges.
From strategy and research to UX design and agile development, we deliver and validate impactful solutions using lean, human-centered practices.
We are proud to be a ‘Great Place to Work®’ certified company for the last three consecutive years. We also hold a top Glassdoor rating and are named among the "Top 50 Most Promising Healthcare Solution Providers" by CIOReview. As an organisation, we foster creativity, continuous learning and inclusivity, creating an environment where bold ideas thrive and make a measurable difference in people’s lives.
Your Mission
We’re seeking a highly experienced, technically exceptional Lead AI Engineer to architect and deliver next-generation Generative AI and Agentic systems. You will drive end-to-end innovation, from model selection and orchestration design to scalable backend implementation, all while collaborating with cross-functional teams to transform AI research into production-ready solutions. This is an individual-contributor leadership role for someone who thrives on ownership, fast execution and technical excellence. You will define the standards for quality, scalability and innovation across all AI initiatives.
What You’ll Do
Develop AI Applications & Agentic Systems
Architect, build and optimise production-grade Generative AI and agentic applications using frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, n8n, Pydantic AI or custom orchestration layers integrating with LLMs such as GPT, Claude, Gemini as well as self-hosted LLMs along with MCP integrations
Implement Retrieval-Augmented Generation (RAG) techniques leveraging vector databases (Pinecone / ChromaDB / Weaviate / pgvector / etc), search engines such as ElasticSearch / Solr using both TF/IDF BM25 based full text search as well as similarity search techniques
Implement guardrails, observability, fine-tune and train models for industry or domain specific use cases
Build multi-modal workflows using text, image, voice and video
Design robust prompt & context engineering frameworks to improve accuracy, repeatability, quality, cost and latency
Build supporting microservices and modular backends using Python or JavaScript or Java aligned with domain driven design, SOLID principles, OOP, and clean architecture using various databases including relational, document, Key-Value, Graph and other types of databases and event driven systems using Kafka / MSK, SQS, etc
Cloud native deployments in hyper-scalers such as AWS / GCP / Azure using containerisation and orchestration with Docker / Kubernetes or serverless architecture
Apply industry best engineering practices: TDD, well-structured and clean code with linting, domain driven design, security-first design (secrets management, rotation, SAST, DAST), comprehensive observability (structured logging, metrics, tracing), containerisation & orchestration (Docker, Kubernetes), automated CI/CD pipelines (Ex: GitHub Actions, Jenkins)
AI Assisted Development, Context Engineering & Innovation
Use AI-assisted development tools such as Claude Code, GitHub Copilot, Codex, Roo Code, Cursor to accelerate development while maintaining code quality and maintainability (not vibe coding, but by a structured approach to AI assisted development)
Utilise coding assistant tools with native instructions, templates, guides, workflows, sub-agents and more to create developer workflows to improve development velocity, standardisation, reliability across AI teams.
Focus on ensuring industry best practices to develop well-structured code that is testable, maintainable, performant, scalable and secure (no compromise)
Partner with Product, Design and ML teams to translate conceptual AI features into scalable user-facing products
Provide technical mentorship and guide team members in system design, architecture reviews and AI best practices
Lead POCs, internal research experiments, and innovation sprints to explore and validate emerging AI techniques
What You Bring
7–12 years of total experience in software development, with at least 3 years in AI/ML systems engineering or Generative AI
Experience with cloud native deployments and services in AWS / GCP / Azure with the ability to architect distributed systems
A ‘no-compromise’ attitude with engineering best practices such as clean code, TDD, containerisation, security, CI/CD, scalability, performance and cost optimisation
Active user of AI-assisted development tools (Claude Code, GitHub Copilot, Cursor) with demonstrable experience using structured workflows and sub-agents
A deep understanding of how LLMs work, context