Artificial Intelligence Engineer

InSync Analytics Corp.; Mumbai Suburban; Hybrid; INR 1,800,000 - 3,000,000; Full time

Job summary

InSync Analytics Corp. is seeking a proficient engineer to design, build, and deploy end-to-end agentic AI workflows that orchestrate LLM reasoning with deterministic components. You will extend tool layers, ensure traceability, and reduce hallucinations while meeting latency and reliability goals.

You will collaborate with analysts and product teams to implement robust data pipelines, APIs, and evaluation harnesses, driving measurable improvements in model safety and performance.

Qualifications

• 3+ years of software engineering, AI/ML, or data engineering.
• Experience delivering agentic AI projects with multi-step workflows.

Responsibilities

• Design, build, and ship agentic workflows for Weave: orchestration, tool selection, retries, failure handling, and handoffs between LLM reasoning and deterministic code.
• Extend the tool layer, including MCP server, tool interfaces and schemas used by models.
• Engineer against hallucination with verifiable pipelines and traceability.
• Build evaluation harnesses and golden datasets; regression testing as a core activity.
• Work with LLMs across the stack: prompts, retrieval, structured outputs, and fine-tuning where appropriate.
• Maintain data pipelines, databases, and APIs essential to Weave and other platforms.
• Optimize latency, token cost, and reliability across long multi-step runs.
• Support deployment, testing, monitoring, and observability of AI systems; diagnose failures.

Skills

Python

Backend engineering

Asynchronous processing

Streaming workflows

Production code

Agentic AI experience

Tools

NumPy

Pandas

PyTorch

TensorFlow

Job description

Role & responsibilities :

Design, build, and ship agentic workflows for Weave: orchestration, tool selection, retries, failure handling, and the handoffs between LLM reasoning and deterministic code.

Build and extend the tool layer, including our MCP server, designing tool interfaces and schemas that models use correctly under real conditions.

Engineer against hallucination: structure pipelines so the agent retrieves and reasons but never invents a number, with verification and traceability built in.

Build evaluation harnesses and golden datasets, and hold new work to them. Regression testing is a first-class part of the job here, not an afterthought.

Work with LLMs across the stack, including prompt and context engineering, retrieval, structured outputs, and fine-tuning where it is genuinely the right tool.

Build and maintain the data pipelines, databases, and APIs that Weave and our other platforms depend on.

Optimise for the constraints that actually bite: latency, token cost, and reliability across long multi-step runs.

Support deployment, testing, monitoring, and observability of AI systems in production, and diagnose failures when they happen.

Work directly with our analysts and product team to understand what a correct model or answer looks like, and encode that judgement into the system.

Prototype and evaluate new AI tools, frameworks, and techniques, and document what you find clearly enough that the team can act on it.

Preferred candidate profile

[2+ years] of professional experience in software engineering, AI/ML, or data engineering, with strong Python and backend engineering fundamentals, including asynchronous processing and streaming workflows. You write production code, not only notebooks.

Demonstrated hands-on experience delivering real Agentic AI projects, meaning agents that plan, make decisions, invoke tools, manage context, and complete multi-step workflows end to end. You have seen these systems fail in production and understand why.

Practical fluency in prompt and context engineering: structuring context, decomposing tasks, and knowing when a prompt is the wrong solution.

Practical experience with agent orchestration patterns such as routing, delegation between agents, memory, structured outputs, and human-in-the-loop workflows.

Solid grounding in machine learning and AI fundamentals, and comfort with libraries such as NumPy, Pandas, and PyTorch or TensorFlow.

Experience integrating agents with production APIs, databases, enterprise applications, and external services.

An empirical instinct: you reach for an evaluation before an opinion, and you are willing to say a system is not ready.

Experience implementing guardrails, permissions, and secure tool execution, plus failure handling, observability, and agent evaluations.

Clear written and verbal communication, including with people who are not engineers.

Familiarity with Agentic AI technologies such as MCP, Gemini, the OpenAI Agents SDK, LangGraph, Anthropic's tooling, or comparable frameworks.

Ability to walk us through your personal contributions to at least one substantial or production Agentic AI implementation, covering its architecture, tools, safeguards, challenges, evaluation approach, and measurable results.

Note on scope: basic chatbots, prompt engineering alone, simple RAG pipelines, tutorials, certifications, and thin wrapper applications are not considered sufficient Agentic AI experience.

GOOD TO HAVE

Contributions to open-source agent tooling, MCP servers, or model-facing tool interfaces used by others.

Experience with financial data, spreadsheets, Excel automation (openpyxl, Office.js), or document-analysis workflows.

Background in finance, fintech, or financial data, so you know what consensus, guidance, and a segment KPI are, and why close enough is worthless in this domain.

Depth in LLM tracing and evaluation infrastructure, building the harnesses and golden datasets rather than just reading the dashboards.

Applied ML or research background: forecasting, time-series modelling, or information extraction from filings and unstructured documents.

Cloud deployment and CI/CD experience.