AI SENIOR SOFTWARE ENGINEER

About Impact Analytics Impact Analytics is an agentic-first AI software company transforming retail merchandising through cutting-edge AI, LLMs, and Generative AI technologies. As a fast-growing Series D company with deployments across five continents, it is building both industry-leading merchandising solutions and foundational AI agents that are redefining how retail decisions are made. What makes Impact Analytics unique is its combination of deep retail domain expertise, strong innovation culture, and global presence. It is one of the few India-born AI companies recognized globally by organizations like Fortune, Gartner, and the Inc. 5000.For candidates looking to work on next-generation AI products with global scale and real-world impact, Impact Analytics is an exciting place to build your career. Here’s a link to our website: www.impactanalytics.co.

Overview The AI Senior Software Engineer builds and ships production-grade AI features within a defined domain of the company’s applied AI platform spanning our GenAI-powered reasoning and analytics platform, our agentic AI authoring and runtime platform, and emerging multimodal / computer vision workloads. You take a prioritised backlog and the architectural guidelines set by the AI Lead and AI Architect and turn them into shipped, well-tested AI systems. This is a hands-on individual-contributor role. You write and review production code, contribute to design decisions within your squad, and may informally guide one or two junior engineers. You do not own a domain’s architecture or lead a squad you implement and evolve features within it, flag issues upward, and own the quality of your own work.

Key Responsibilities

• Platform Implementation
• Implement and evolve features across the GenAI reasoning and agentic AI platform in alignment with the reference architecture defined by the AI Architect.
• Contribute to the technical design of your squad’s workstreams — data flow, API contracts, component boundaries, and integration points with upstream/downstream systems.
• Write and review production-quality Python code; follow code standards, participate in PR reviews, and help maintain a clean, well-tested codebase.
• Contribute input to build-vs-buy-vs-open-source evaluations for your features; provide trade-off analysis to the AI Lead / AI Architect, who own the final decision.
• Contribute to Architecture Decision Records (ADRs) for decisions within your scope; review ADRs as requested.
• Agentic AI Systems
• Build and ship agent workflows (ReAct, planner–executor, reflection, human-in-the-loop checkpoints) using the chosen orchestration framework (LangGraph / LlamaIndex Workflows / Semantic Kernel or equivalent).
• Implement tool and MCP (Model Context Protocol) integrations for connecting agents to enterprise systems (CRM, ITSM, REST/GraphQL APIs) with scoped auth (OAuth2/OBO), sandboxing, and rate limiting.
• Contribute to agent evaluation coverage: task-completion benchmarks, tool-call success rates, trajectory evals, and cost-per-task regression tests.
• Implement agent memory patterns (short-term conversation context, long-term episodic/semantic memory) following TTL and retrieval policies defined by the AI Lead / AI Architect.
• GenAI & RAG Systems
• Implement and iterate on RAG pipelines: hybrid retrieval (BM25 + dense + rerankers), query decomposition, GraphRAG for knowledge-graph workloads, and citation grounding.
• Execute on the fine-tune vs. prompt vs. in-context-learning decision set by the AI Lead / AI Architect; carry out fine-tuning runs (LoRA/QLoRA/PEFT) with guidance when required.
• Apply context engineering patterns: prompt contracts, structured outputs (JSON Schema, Pydantic, function-calling), constrained decoding, output validators, and fallback chains.
• Implement grounding and hallucination-mitigation mechanisms: retrieval confidence scoring, “I don’t know” paths, claim-level validators.
• Multimodal & Computer Vision (Contextual Scope)
• Where the product roadmap requires it, build VLM-first pipelines (GPT-4V class, Gemini, Qwen-VL) and integrate CV outputs (OCR, document understanding, layout parsing) as tools inside the agentic platform.
• Run benchmarks on open-weight VLMs (Qwen-VL, InternVL) vs. closed VLM APIs for your squad’s use cases; report findings with cost-latency-accuracy data.
• LLMOps, Evaluation & Observability
• Instrument telemetry for the features you build: token cost, latency p50/p95, tool-ca…