Newpage - Full Stack Developer

Newpage Solutions; Maharashtra; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

Newpage Solutions is seeking an experienced AI engineer to build production-grade, agentic software. You will design, implement, and operate end-to-end AI-powered workflows and modular agents, ensuring reliability and scalability.

The role requires hands-on work with LangGraph, AutoGen, and cloud deployments across AWS/Azure, with strong Python/TypeScript skills and a commitment to clean code, testing, and observability.

Qualifications

• 3+ years building production AI applications and agentic workflows.
• Wired tools to a model for multi-step execution with orchestration.
• Active use of AI-assisted development tools with demonstrable workflows.
• Strong Python or TypeScript with 12-factor apps and microservices.
• End-to-end eval harnesses and vector databases.
• Cloud-native deployment on AWS/Azure/Cloudflare/Vercel with Docker/Kubernetes.
• Commitment to clean code, TDD, security, observability, cost.
• Deep understanding of LLM behavior and optimization.
• Strong communication and problem reframing.
• Public portfolio or OSS contributions.
• Founder mindset and appetite for ambiguity.
• Bachelor's or Master's in CS/ML.
• Public writing or talks about AI.
• MLOps and model serving (BentoML, MLflow, Vertex AI, SageMaker).
• Streaming and batch pipelines (Spark, Airflow, Beam, Glue).
• Healthcare or life sciences exposure.
• AWS certification.

Skills

Agentic AI

Python

TypeScript

Next.js

FastAPI

MLOps

Docker

Kubernetes

GitHub Actions

Cloud deployment

Education

BSc/ MSc in CS/ML

Tools

LangGraph

AutoGen

Claude Agent SDK

OpenAI Assistants

GitHub Copilot

Job description

Test, Deploy, Productionize

Spin up the infra, write the evals, wire up the MCP servers, deploy the agents, and harden the bits that survive contact with real users.

Deploy on AWS, Azure, Cloudflare, or Vercel using containerization (Docker, Kubernetes) or serverless chosen for fit, not preference.

Treat evals as a first-class discipline: hands-on harnesses, not theoretical frameworks.

Build with a clear-eyed view of where current AI tooling helps and where it falls short.

Apply engineering practices that hold up in production: TDD, secrets management and rotation, SAST/DAST, structured logging, metrics, tracing, and automated CI/CD (GitHub Actions, Jenkins).

Own what you build end-to-end, including the infrastructure and operations that keep it running.

Mentor others on system design, agentic patterns, and AI engineering best practices.

Required

What You Bring

• 3+ years relevant experience building production applications using AI / agentic development approaches-fullstack applications, agents, workflows, MCPs, and more.
• Hands-on experience with agents, not just prompted models. You have wired tools to a model and let it run multi-step using LangGraph, AutoGen, Claude Agent SDK, OpenAI Assistants, or your own orchestration.
• Active, structured use of AI-assisted development tools (Claude Code, Cursor, GitHub Copilot) with demonstrable workflows, sub-agents, skills, and innovative approaches.
• Strong Python or TypeScript, with OOP, SOLID, 12-factor application development, and microservice architecture. You've built Next.js applications, FastAPI services, and similar.
• End-to-end implementation experience with vector databases, retrieval pipelines, and eval harnesses.
• Cloud-native deployment experience across at least one of AWS, Azure, Cloudflare, or Vercel-with Docker, Kubernetes, and GitHub Actions.
• A no-compromise attitude on clean code, TDD, security, observability, scalability, performance, and cost.
• A deep working understanding of how LLMs behave-and where they break-and how to optimize accuracy, latency, and cost.
• Clear writing and a willingness to reframe problems in conversation rather than wait for someone else to define them.
• A real, recent trail of built things : GitHub, a portfolio, side projects, indie tools, or OSS contributions.
• A founder's mindset and genuine appetite for ambiguous, high-impact technical challenges.
• Bachelor's or Master's in Computer Science, Machine Learning, or a related technical discipline.
• Public writing, talks, or threads about building with AI.
• MLOps and model serving experience (BentoML, MLflow, Vertex AI, SageMaker).
• Streaming and batch ingestion pipelines (Spark, Airflow, Beam, Glue).
• Healthcare or life sciences domain exposure.
• AWS Professional certification or other relevant industry certifications

(ref:hirist.tech)