Job description:
Role Overview
We are looking for an experienced AI Engineer to build advanced Agentic AI and reasoning systems for complex financial and business use cases. The role focuses on developing AI systems that can reason over complex financial data, legal documents, and market contexts rather than relying only on traditional pattern matching.
You will work on multi-agent architectures, LLM evaluation, prompt optimization, data pipelines, memory systems, observability, and cloud infrastructure to build reliable, scalable, and production-ready AI solutions.
Key Responsibilities
- Design and implement stateful, multi-agent pipelines capable of performing complex analysis and reasoning.
- Develop and optimize advanced prompt chains and inference pipelines using frameworks such as DSPy and GEPA.
- Experiment with reasoning techniques including Chain-of-Thought, Tree-of-Thought, and Graph-of-Thoughts.
- Build rigorous AI evaluation systems using LLM-as-a-Judge and scenario-based testing.
- Measure model accuracy, robustness, reasoning quality, latency, cost, and reliability.
- Architect memory, retrieval, and orchestration strategies for multi-agent workflows.
- Implement Human-in-the-Loop (HITL) workflows for high-stakes AI decisions.
- Build complete AI observability and traceability systems, including agent actions, tool calls, intermediate outputs, and system performance.
- Develop ETL and data engineering pipelines for structured, unstructured, vector, and graph data.
- Work with SQL/NoSQL, vector databases, and graph-based data systems.
- Build dashboards to monitor cost, latency, correctness, system performance, and concept drift.
- Deploy and maintain AI services across AWS and/or Azure environments.
- Integrate AI systems with modern DevOps and CI/CD pipelines.
- Collaborate with engineering and product teams to design scalable and reliable AI architectures.
Required Qualifications
- 5+ years of professional software development experience using Python or JavaScript.
- Proven experience building and deploying production-grade Agentic AI, LLM, or complex reasoning systems.
- Strong hands-on experience with modern LLM Ops and AI engineering frameworks, such as:
- LangChain
- Langfuse
- Weights & Biases (W&B)
- Helicone
- Strong understanding of AI agent architectures, including:
- Prompt engineering
- RAG
- Memory
- Tool integration
- Human-in-the-loop workflows
- Multi-agent systems
- MCP
- Strong data engineering experience with:
- ETL pipelines
- SQL/NoSQL databases
- Vector databases
- Graph data models
- Proficiency with AWS and/or Azure.
- Experience with modern DevOps practices including CI/CD, Docker/containerization, and Infrastructure as Code.
- Strong problem-solving skills and the ability to design systems for complex, high-stakes use cases.
Nice to Have
- Experience with RLHF/RLAIF pipelines.
- Experience with model fine-tuning techniques such as LoRA or QLoRA.
- Experience working with graph databases and graph-based reasoning.
- Experience building AI systems for finance, lending, credit, risk, or other regulated industries.
- Experience designing evaluation frameworks for reasoning-heavy AI applications.
What You Will Work On
- Building advanced Agentic AI systems rather than simple chatbot applications.
- Developing reasoning architectures capable of handling complex, multi-step problems.
- Designing foundational evaluation, observability, memory, and orchestration infrastructure.
- Working with large volumes of structured and unstructured financial data.
- Building reliable AI systems where accuracy, traceability, and risk management are critical.
- Taking significant ownership of architecture, experimentation, and technical implementation.
Key Skills
Python | JavaScript | Agentic AI | LLMs | LangChain | DSPy | GEPA | RAG | Multi-Agent Systems | Prompt Engineering | LLM Evaluation | Langfuse | W&B | Helicone | MCP | ETL | SQL | NoSQL | Vector Databases | Graph Databases | AWS | Azure | Docker | CI/CD | LoRA | QLoRA | RLHF/RLAIF
Pay: ₹1,500,000.00 - ₹2,500,000.00 per year
Work Location: Remote
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