AI Engineer

AI SSE — Agentic AI Systems Location: Bangalore Experience: 3–8 years Type: Hands-on IC | Systems Builder | Production Owner **ABOUT GOCOMET **GoComet is a Series B logistics technology company that helps global enterprises like Unilever, Honda, Tata, and Schneider optimize freight spend, automate procurement, and build resilient supply chains. We're pivoting hard into named-enterprise accounts with a 4.2x growth engine. Our product depth across procurement, planning, execution, and audit gives us a quantifiable ROI story competitors can't match. If you want to live inside LLMs, prompts, orchestration, and production complexity — this is your role. **Why This Role Exists** Most teams stop at demos. Production agentic systems fail because: ● Prompts drift ● Memory breaks ● Tools misfire ● Costs explode ● Edge cases compound We need engineers who understand: LLMs as distributed systems with probabilistic components. You will make agentic AI reliable. **What You Will Own** Core Intelligence Layer ● Prompt architecture & evaluation ● Tool calling reliability ● Memory design (short-term / long-term / retrieval) ● Planning & execution loops ● Guardrails and verification Production Systems ● Latency, cost, observability ● Failure handling & retries ● Agent debugging pipelines ● Versioning prompts & behaviors ● Continuous evaluation **Outcome Delivery** ● Translate product intent into working agents ● Work with PMs to define constraints ● Ship systems that work under ambiguity You don’t stop at: “Agent works once.” You stop at: “Agent works reliably at scale.” **What You’ll Build** ● Multi-agent architectures (planner / executor / verifier / critic) ● Tool ecosystems for agents ● Evaluation harnesses for LLM behavior ● Memory & retrieval systems ● Agent simulation environments ● Cost-aware orchestration layers ● Self-improving feedback loops **How You’ll Work** Deep LLM Engineering ● Prompt design patterns ● Structured output strategies ● Function/tool calling ● Failure mode mitigation ● Model selection & routing ● Fine-tuning / RAG / hybrid approaches Software Engineering ● Python / TypeScript production systems ● Async orchestration ● Distributed tracing ● CI for agent behavior ● Infra for evaluation *This Is Not Just Backend Engineering This is intelligence engineering. You’re not wiring APIs. You’re building systems that reason, act, recover, and improve.* **Your success is measured by:** ● Reliability of agent behavior ● Latency & cost efficiency ● Failure recovery ● Real outcomes delivered at scale **Debugging Intelligence** You will debug: ● Silent hallucinations ● Agent loops ● Tool misuse ● Memory corruption ● Drift across model versions ● Cost anomalies **Who You Are** *You don’t ask: “What endpoint should I build?” You ask: “Why did the agent fail on this edge case?”* **Must-Have** ● 4–8 years strong software engineering ● Deep hands-on LLM experience in production ● Strong understanding of: ○ Prompting strategies ○ Agent orchestration ○ Memory/RAG ○ Tool calling ○ Evaluation ● Can independently design and ship AI systems ● Strong Python or TypeScript ● Obsessed with reliability **Nice to Have** ● Built multi-agent systems ● Worked on workflow automation ● Built internal copilots ● Experience with evaluation frameworks ● Experience with cost optimization What This Role Is NOT ● Not model research ● Not API wrapper work ● Not demo building ● Not prompt hacking only ***This is:*** *Engineering intelligence into production systems.*