Applied AI Engineer

You will build the intelligence layer of Aiva: the LLM-powered pipelines that turn raw agent interactions into structured, durable memory — and decide what an agent should remember, consolidate, retrieve, or forget. Where the retrieval engineer makes memory fast, you make it smart. You will work daily with Claude on Bedrock, and with Kubogent-fine-tuned small models where latency and cost demand them, and you will own the evaluation harnesses that prove memory quality actually improves agent outcomes.

Key Responsibilities

• Build memory extraction pipelines: turning conversations, documents, and process events into structured facts, entities, relationships, and episodic summaries using LLMs (Claude on Bedrock) and NLP techniques.
• Design and implement memory consolidation: merging duplicate or conflicting facts, progressive summarization of long histories, salience scoring, and decay/forgetting policies.
• Develop context assembly logic: given an agent task and token budget, select, rank, and compress the right memories into the prompt — balancing relevance, recency, and cost.
• Build the Aiva evaluation stack: retrieval-quality benchmarks (precision/recall of remembered facts), memory-drift and contamination tests, hallucination checks, and A/B measurement of downstream agent task success.
• Prototype and productionize embedding strategies: model selection, chunking, hybrid sparse+dense representations, and re-ranking.
• Partner with the Kubogent team to fine-tune and deploy small models for high-volume memory tasks (extraction, classification, summarization) where frontier-model calls are too slow or costly.
• Instrument PII detection and redaction in memory pipelines, working within AI Gateway/ReVAct governance policies.
• Stay current with memory-architecture research (episodic/semantic memory, GraphRAG, agentic memory frameworks) and translate it into shipped capability.

Must-Have Qualifications

• 4+ years of software engineering with strong Python, including 2+ years building LLM or NLP applications in production.
• Hands-on experience with prompt engineering, structured output extraction, RAG pipelines, and embedding models — beyond demos, with real users and real failure modes.
• Experience building evaluation frameworks for LLM systems: golden datasets, LLM-as-judge patterns, regression suites, and offline/online metric design.
• Solid ML fundamentals: retrieval metrics, ranking, classification, and enough statistics to design and interpret A/B tests.
• Working knowledge of AWS ML stack (Bedrock, SageMaker) or equivalent cloud ML platforms.
• Strong software craftsmanship: testing, versioning of prompts/models/datasets, and reproducible pipelines.

Nice-to-Have

• Experience fine-tuning small/open models (LoRA/QLoRA, distillation) and serving them efficiently.
• Familiarity with agent memory frameworks and research (e.g., GraphRAG, MemGPT-style architectures, reflection/consolidation patterns).
• Knowledge-graph construction experience: NER, relation extraction, entity linking.
• Multilingual NLP experience relevant to Indian enterprise customers (Hindi, Tamil, and other Indic languages).
• Published work, open-source contributions, or strong writing on applied LLM systems.