Project Role : AI Native Software Engineer

Project Role Description : Build and ship production-ready software using AI as a core part of the engineering lifecycle. Apply AI-native practices to develop and validate solutions, maintain quality and compliance, and continuously improve how software is built and operated.

Must have skills : .Net Full Stack Development

Good to have skills : NA

Minimum 5 Year(s) Of Experience Is Required

Educational Qualification : 15 years full time education

Role Description

You build the systems that actually make AI work in enterprise environments, not demos, not prototypes that stall after a pilot, but production agentic architectures running inside real client organizations. The difference between an AI Engineer and what we are looking for is straightforward: you have shipped a multi-agent system in production, you have owned the eval harness, and you know what happens when your agent fails at 2am because you have lived it.

As an AI Engineer (Agentic/Applied), you will design, build, and deploy production-grade agentic AI systems across the full enterprise technology stack. You will work directly with client engineering teams, lead technical design sessions, and build reusable patterns and accelerators that scale beyond individual engagements.

This role sits at the heart of the AI engineering talent market — demand is growing faster than supply and will continue to do so. We offer what no single product company can: breadth across every industry, every enterprise technology stack, and every level of organizational complexity, combined with vendor fellowship access inside Anthropic, OpenAI, Microsoft, and Google engineering teams and a direct pathway to the Forward Deployed Engineer programme.

Key Responsibilities

Design and build production-grade agentic systems end-to-end: multi-agent orchestration, RAG pipelines, policy-based routing, tool invocation, memory management, and lifecycle observability

Build and own RAG pipelines: embeddings, chunking strategy, vector search, context window engineering and tuning against real quality targets

Integrate and abstract across multiple LLM providers — OpenAI, Anthropic, Vertex AI, and open-source models — with fallback routing, token, cost, and latency management

Implement LLMOps in production: eval harnesses with real quality metrics, prompt versioning, observability tooling (LangSmith, Braintrust, or equivalent), cost and safety monitoring

Embed directly with client engineering teams to design, prototype, and deploy agentic solutions — workshops, proofs of concept, code-with sessions, and architecture walkthroughs

Build reusable patterns, accelerators, and playbooks that scale beyond the individual client engagement and enable the next one to start faster

Define and use metrics to measure agent accuracy, latency, safety, and cost-effectiveness present findings and recommendations to client stakeholders in business terms

Basic Qualifications

5 – 7.5 years of software engineering experience in production environments

Proficiency in at least one primary backend language: Python, Java, & .Net Full Stack or TypeScript

Minimum 1 year of hands-on experience designing and deploying agentic AI solutions in a production environment — non-negotiable

Demonstrated experience with agentic orchestration frameworks: LangGraph, CrewAI, AutoGen, or equivalent — at production depth, not tutorial level

Direct experience calling LLM APIs (OpenAI, Anthropic, Vertex AI) in production code: provider abstraction, token management, latency and cost tradeoffs

RAG pipeline ownership: embeddings, chunking strategy, vector databases, and context engineering

LLMOps fundamentals: eval harness design, prompt versioning, and production observability

Cloud-native engineering maturity: Kubernetes, Docker, microservices, serverless, CI/CD, and IaC (Terraform or Helm)

Strong Python Java or equivalent backend language acceptable production debugging and observability experience

Quality of experience is weighted over years, a candidate who has shipped three production agentic systems in four years is preferred over a generalist with passive AI exposure