Senior Backend Developer

Roles & Responsibilities:

• Lead the design and development of agentic and intelligent systems with strong ownership of core algorithms, model behavior, and decision logic, minimizing reliance on managed AI services and abstraction-heavy frameworks.
• Architect end-to-end agentic systems with deep control over model internals, reasoning flows, tool-use logic, and execution pipelines rather than wrapper-based orchestration.
• Take algorithmic and model-level ownership of agent behavior, including reasoning strategies, planning algorithms, fallback logic, and failure handling.
• Design and implement custom agent orchestration, memory systems, context management, and stateful workflows without over-dependence on LangChain/LangGraph-style abstractions.
• Build and fine-tune open-source and self-hosted LLMs or hybrid approaches (open-weight + proprietary) based on performance, cost, and security trade-offs.
• Develop custom RAG pipelines, embedding strategies, ranking, re-ranking, and grounding mechanisms tailored to enterprise knowledge systems.
• Integrate agentic solutions with enterprise applications, APIs, data platforms, and internal ML systems.
• Establish low-level design patterns, reusable libraries, and internal frameworks for building agent-based systems from first principles.
• Optimize model inference, reasoning efficiency, latency, cost, and response quality, including prompt-free and programmatic reasoning approaches where applicable.
• Drive Responsible AI practices with in-house guardrails, explainability techniques, auditability, and governance controls.
• Collaborate with business stakeholders to translate complex requirements into deterministic, reliable agent-driven solutions, avoiding black-box dependencies.
• Lead production support, deep debugging, RCA, and performance tuning at both system and model levels.
• Mentor junior consultants on core AI concepts, algorithms, and system design, not just framework usage.

Education:

• BE / B.Tech in Computer Science, Engineering, or a related discipline.

Required Experience & Skills.:

• 4 to 7 years of strong experience building AI-driven systems, with demonstrated ownership of model behaviour and algorithms, not just API integration.
• Deep understanding of agentic architectures, multi-agent coordination, autonomous decision-making systems, and custom orchestration approaches.
• Ability to design agent frameworks from scratch or significantly extend existing ones rather than relying on out-of-the-box abstractions.
• Strong understanding of LLM internals, transformer architectures, inference behavior, tokenization, attention, and trade-offs.
• Experience with open-source LLMs (e.g., LLaMA-family, Mistral, Mixtral, Falcon, Qwen) and self-hosted deployments.
• Advanced reasoning strategies: chain-of-thought alternatives, tree/graph-based reasoning, planners, critic-agent loops, and tool reasoning.
• Prompting as a controlled technique, not the primary abstraction.
• Experience with LangChain, LangGraph, AutoGen, CrewAI as reference tools, with proven ability to bypass, customize, or replace them with custom implementations.
• Preference for code-first, algorithm-driven approaches over declarative or black-box pipelines.
• Deep experience in RAG pipelines, embeddings, vector similarity search, hybrid retrieval, reranking models, and knowledge grounding.
• Strong grasp of information retrieval, indexing, and retrieval algorithms.
• Strong systems engineering skills with Python and/or Java/Go for AI backends.
• Experience deploying AI workloads on GCP, including self-managed inference, GPUs, containers, and scaling strategies.
• Familiarity with MLOps practices: model versioning, evaluation pipelines, controlled rollouts, and experimentation.
• Deep experience in AI observability: tracing reasoning paths, latency profiling, quality evaluation, and cost monitoring.
• Strong understanding of AI security, data privacy, access control, and compliance, with preference for in-house controls over managed tooling.
• General Insurance (Health, Motor, Travel) domain knowledge is a strong advantage.

Soft Skills:

• Demonstrates strong ownership mindset, curiosity at the algorithmic level, and comfort working with ambiguity and complex systems.
• Collaborates effectively across engineering, data science, and business teams, clearly explaining technical trade-offs and design decisions.
• Exhibits professional integrity, reliability, and clear, respectful communication with stakeholders.