Newpage Solutions

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.) and search engines such as ElasticSearch / Solr using both TF/IDF BM25-based full-text search and 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, JavaScript, or Java aligned with domain-driven design, SOLID principles, OOP, and clean architecture, using various databases including relational, document, Key-Value, Graph, and event-driven systems using Kafka / MSK, SQS, etc.
• Deploy cloud-native applications 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 (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.
• Utilise coding assistant tools with native instructions, templates, guides, workflows, sub-agents, and more to create developer workflows that improve development velocity, standardisation, and reliability across AI teams.
• Ensure industry best practices to develop well-structured code that is testable, maintainable, performant, scalable, and secure.
• 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 LLMs, context engineering approaches, and best practices, with the ability to optimise accuracy, latency, and cost.
• Python or JavaScript experience with strong grasp of OOP, SOLID principles, 12-factor application development, and scalable microservice architecture.
• Proven track record developing and deploying GenAI/LLM-based systems in production.
• Advanced understanding of context engineering, prompt construction, optimisation, and evaluation techniques.
• End-to-end implementation experience using vector databases and retrieval pipelines.
• Experience with GitHub Actions, Docker, Kubernetes, and cloud-native deployments.
• Obsession with clean code, system scalability, and performance optimisation.
• Ability to balance rapid prototyping with long-term maintainability.
• Excel at working independently while collaborating effectively across teams.
• Stay ahead of the curve on new AI models, frameworks, and best practices.
• Have a founder’s mindset and love solving ambiguous, high-impact technical challenges.
• Bachelor’s or Master’s degree in Computer Science, Machine Learning, or a related technical discipline.

Bonus Skills / Experience

• Understanding of MLOps, model serving, scaling, and monitoring workflows (e.g., BentoML, MLflow, Vertex AI, AWS Sagemaker).
• Experience building streaming + batch data ingestion and transformation pipelines (Spark / Airflow / Beam).
• Mobile and front-end web application development experience.

What We Offer

• A people-first culture – Supportive peers, open communication, and a strong sense of belonging.
• Smart, purposeful collaboration – Work with talented colleagues to create technologies that solve meaningful business challenges.
• Balance that lasts – We respect your time and support a healthy integration of work and life.
• Room to grow – Opportunities for learning, leadership, and career development, shaped around you.
• Meaningful rewards – Competitive compensation that recognises both contribution and potential.