Lead Gen AI & Agentic AI Engineer

• Experience: 5+ years
• Primary Stack: Python, LLM APIs, RAG, agent frameworks
• Cloud: Cloud-agnostic. Deep experience with at least one of AWS, GCP, or Azure, and the ability to work on the others
• Location & Mode: Full-time, Work From Office. Noida, Sector 132
• Company: Binariq (TSCx Consulting Pvt Ltd)

Role Summary

We are looking for a Lead Gen AI & Agentic AI Engineer to own the design and delivery of LLM-powered and autonomous agent systems for enterprise clients. You will take a business problem from an early client conversation through to a production system, making the architecture calls along the way and defending them to both technical and non-technical audiences.

We need someone who has built agents that run against live client systems. That means scoping what an agent can access, deciding where a human has to approve, and handling failures predictably when it acts.

Developing the engineers around you is a core part of this role. You will mentor associate engineers day to day, assist in running Binariq's internal training on Gen AI and agentic practice, and own the technical onboarding path for engineers joining the practice.

Key Responsibilities

Design & Build

• Own end-to-end design of Gen AI solutions: retrieval architecture, model selection, prompt strategy, and integration approach
• Design agentic systems: tool contracts, orchestration logic, state management, and multi-agent decomposition where it is justified
• Define the boundaries of autonomy: what an agent may access, what it may act on, where a human must approve, and how it escalates
• Build guardrails against prompt injection, sensitive data exposure, and unsafe or incorrect output
• Design failure behaviour explicitly: retries, timeouts, circuit breakers, cost ceilings, and rollback paths
• Establish evaluation frameworks for LLM and agent output, and use them to drive iteration rather than relying on impressions
• Treat token and inference cost as a first-class design constraint

Client & Delivery

• Work directly with client stakeholders to shape requirements and explain technical trade-offs
• Support pre-sales and solutioning by estimating effort, assessing feasibility, and identifying risk early
• Set technical direction on engagements and review the team's work against it
• Hand over production-ready systems with the necessary instrumentation and documentation

Training & Capability Building

• Mentor associate engineers through code review, pairing, and design discussion
• Assist in executing Binariq's internal training on Gen AI and agentic engineering
• Own the technical onboarding path for engineers joining the practice, so new joiners reach useful output quickly
• Set and enforce engineering standards across the team, covering evaluation, guardrails, and safe agent design
• Build reusable patterns, reference implementations, and internal documentation the team can work from

Required Technical Skills

Programming & Architecture

• Strong Python, including asynchronous programming, packaging, and testing discipline
• FastAPI or equivalent, with sound API and microservices design
• SQL and NoSQL, with judgement about which fits a given problem
• System design: how the AI components sit within a wider client architecture

Generative AI (depth expected)

• Production experience with multiple LLM providers, and informed opinions about their trade-offs
• Advanced RAG: chunking strategy, embedding model selection, hybrid and semantic search, re-ranking, query rewriting, and diagnosing retrieval failure
• Vector databases: practical experience, including indexing strategy and retrieval performance at scale
• Prompt and context engineering at system level, not just per call
• Evaluation methodology: designing test sets, measuring groundedness and accuracy, and building regression suites for non-deterministic output
• Guardrails and safety: prompt injection defence, output filtering, PII handling in prompts
• LangChain / LlamaIndex, with a clear view of when not to use a framework

Agentic AI (the core of this role)

• Tool and function calling: designing tool contracts, schemas, and error semantics
• Production experience with at least one agent framework: LangGraph, CrewAI, AutoGen, Semantic

Kernel, Google ADK, or equivalent

• Orchestration and state management across long-running, multi-step workflows
• Agent memory: short-term context and persistent state
• Human-in-the-loop design: approval gates, escalation, and deciding where autonomy should stop
• Permissioning and sandboxing: scoping agent access to systems and data
• Failure recovery and loop control: preventing runaway execution and runaway cost
• Tracing and observability for agent runs (Langfuse, LangSmith, Arize Phoenix, or similar)
• MCP (Model Context Protocol)
• Multi-agent architecture, and the judgement to know when a single agent is the better answer

Cloud

• Deep hands-on experience with at least one of:
• AWS: Bedrock, SageMaker, Lambda
• GCP: Vertex AI, Cloud Run
• Azure: Azure OpenAI, Azure AI Search, Azure ML
• Ability to become productive on the others, since client stacks vary
• IAM, secrets management, and cloud security fundamentals
• Docker, and enough CI/CD literacy to hand over cleanly

Good to Have

• Fine-tuning open-source models (LoRA, PEFT, QLoRA) and a view on when fine-tuning beats retrieval
• Lightweight AI/ML Ops exposure: enough monitoring to know when something has broken in production
• Self-hosted and open-weight deployment (vLLM, Ollama, Hugging Face), relevant for data residency and air-gapped clients
• Multi-modal systems: document, image, or voice
• Classic predictive ML, for engagements that combine it with Gen AI
• Regulated-domain delivery experience (BFSI, healthcare, public sector, defence)
• Consulting or client-facing delivery background
• Conference talks, published writing, or open-source contribution

Experience Requirements

• 5+ years of overall software engineering experience
• 3+ years hands-on with Gen AI or LLM-based application development
• Hands-on agentic build experience: a system that called tools and took real actions across services, rather than one that only generated text. Production deployment with real users is strongly preferred; a substantial internal or proof-of-concept build will be considered where the candidate can explain the permissioning model, the failure handling, and what they would change to take it to production
• Demonstrable ownership of architecture decisions, including ones that turned out to be wrong and what you changed
• Experience working directly with business stakeholders
• Demonstrable experience developing other engineers, through mentoring, technical leadership, or running training. Experience building a curriculum or onboarding programme is a strong advantage

What We're Looking For

• Architecture judgement. Knowing when a simple RAG pipeline beats an agent, and being willing to say so to a client who asked for an agent.
• Commitment to developing others. Raising the level around you is part of the role, not an extra. We are building a practice, and the team's depth matters as much as any single engagement.
• Caution with autonomy. Understanding the risk of letting an agent act on a client's live systems, and designing for it.
• Evaluation discipline. Measuring output quality with test sets and defined metrics.
• Portable fundamentals. Our work spans different clients, clouds, and constraints. We look for people who understand how things work underneath, so their skills transfer.
• Clear communication. You will explain technical limitations and trade-offs to client stakeholders who do not share your background.

Notes on Scope

This is a build role, not a platform role. Owning client production infrastructure, platform monitoring, and cost governance at scale is not what we are hiring for. That said, you should be comfortable deploying and running your own work, and you are expected to instrument and document your systems properly.