AI Solutions Engineer

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.

Interested?

Send us your resume and we'll get back to you.