AI Solutions Engineer

Needl.ai LLP; Bengaluru; Hybrid; INR 2,500,000 - 4,000,000; Full time

Job summary

Needl.ai LLP in Bengaluru, India, is seeking an experienced engineer to build and deploy agentic AI solutions for enterprise customers. You will own the full customer journey from discovery through deployment and success, designing autonomous agents and workflows that solve high-value knowledge problems.

The role requires hands-on experience with frontier LLMs, Python-based development, and collaboration with product, engineering, and customer success teams to ship production-grade solutions.

Qualifications

• 2+ years of software engineering experience with agents and frontier LLMs.
• Production systems: shipping LLM-powered agentic solutions to customers.
• Strong Python (backend) and JavaScript (frontend) skills.
• Deep practical understanding of agent orchestration, tooling calls, and LLM evaluation.

Responsibilities

• Architect, build, and productionize agentic AI solutions for enterprise workflows.
• Own end-to-end lifecycle: design, implementation, deployment, monitoring, iteration.
• Develop Python-based AI solutions with prompt engineering and tool calls.
• Design evaluation frameworks for reliability, latency, and cost.
• Lead client engagement, discovery, and translate requirements into design.

Skills

Python

JavaScript

Agent orchestration

ReAct loops

LLM integration

Cloud deployment

Tools

Elasticsearch

OpenTelemetry

Vector search

BM25

AWS

Azure

GCP

Job description

Join Needl.ai to build and ship agentic AI solutions for our enterprise customers as a Forward Deployed / Solutions Engineer. This role sits at the intersection of hands-on agent engineering and customer-facing deployment. You'll design, build, and productionize autonomous AI agents and workflows that solve high-value knowledge problems for leading enterprises, owning the complete customer journey from discovery through deployment and success.

We're looking for an experienced engineer who has built agentic systems with frontier LLMs and shipped them into real customer-facing production environments, and who thrives working directly with enterprise customers.

Agentic AI development & technical implementation

• Architect, build, and productionize agentic AI solutions (tool-using agents, ReAct loops, multi-step reasoning pipelines, RAG systems) tailored to enterprise customer workflows.
• Own the end-to-end lifecycle of agent systems: design, implementation, evaluation, deployment, monitoring, and iteration in production.
• Develop robust AI-driven solutions in Python, including advanced prompt engineering, agent orchestration, tool/function calling, and integration with frontier LLM APIs and MCP servers.
• Design and maintain evaluation frameworks to measure and improve agent reliability, accuracy, latency, and cost on real customer use cases.
• Build customer-facing interfaces using JavaScript where needed.
• Drive system integration and deployment on cloud platforms (Azure, AWS), with attention to reliability, observability, and guardrails.
• Establish and contribute to internal engineering playbooks, best practices, and reusable frameworks for agent development.
• Collaborate closely with product, engineering, and customer success teams, and mentor junior engineers and interns.

Customer solution design, deployment & success

• Lead client engagement and discovery
  • Run customer calls, analyze requirements, and translate complex business workflows and knowledge-intensive processes into technical solution designs.
• Own solution design and prototyping
  • Rapidly design and prototype agentic AI solutions using the internal tech stack, then harden them for production.
• Drive deployment and customer success
  • Own solution implementation, monitoring, and post-deployment optimization, including tuning agent behaviour and leading customer success reviews.
• Own documentation and feedback loops
  • Create customer documentation, implementation guides, and best practices while collecting and analyzing feedback to inform product improvements.

Required qualifications

• Experience: 2+ years of professional software engineering experience, including hands-on work building agents and ReAct loops with frontier LLMs (Claude, GPT, Gemini).
• Production systems: demonstrated experience building and shipping LLM-powered / agentic systems into customer-facing production environments, with attention to reliability, evaluation, and guardrails.
• Programming proficiency: strong Python skills (back-end); working familiarity with JavaScript (front-end).
• Agentic AI expertise: deep, practical understanding of agent orchestration, tool/function calling, RAG, prompt engineering, context management, and LLM evaluation.
• Cloud: solid understanding of cloud platforms (AWS/Azure/GCP) and production deployment concepts.
• Interest in finance, fintech, or other knowledge-intensive industries.
• Strong customer-facing skills and the ability to translate ambiguous business problems into working technical solutions.
• Enthusiasm for agentic AI and a drive to stay at the frontier of this fast-evolving space.

Nice to have

• Experience with observability and instrumentation for AI systems (e.g. OpenTelemetry, tracing, evals in production).
• Experience with search infrastructure (e.g. Elasticsearch) and hybrid retrieval (BM25 + vector).
• Prior forward-deployed, solutions engineering, or customer-facing engineering experience.

What we offer

• Work at the cutting edge of generative AI, context engineering, and agentic/knowledge-agent technologies.
• Work directly with leading clients in the finance industry, solving real-world challenges.
• Exposure to C-level executives and decision-makers at enterprise clients and Needl.ai.
• A collaborative work environment that values responsibility, curiosity, and growth.
• Own live customer projects and ship agents that affect real business outcomes.
• Contribute to solutions serving leading enterprises in finance and emerging tech sectors.
• Build a portfolio of deployed agentic AI solutions across diverse use cases.

Selection criteria

• Technical assessment: Python programming, agent design, and AI/ML fundamentals.
• Agentic AI system design: architecting a production-grade agent or LLM-powered workflow for a knowledge-intensive customer problem.
• Cultural fit: alignment with our values of responsibility, curiosity, and customer obsession.