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Atharva Pandkar
Junior AI/ML Engineer specializing in LLM agents and RAG systems
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About
Built and deployed a production, multi-tenant modular agentic AI platform at Easybee AI, using LangChain/LangGraph with Redis-backed durable state to make agents reusable, traceable, and auditable. Emphasizes reliability via strict tool schemas, deterministic controllers, tenant-level policy enforcement, and regression testing derived from real production failures; also delivered AI automation for legal/finance workflows (attorney draw and expense automation) with explainable, deterministic payouts.
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Key Strengths
- Built and deployed a modular, reusable agent platform to production (reduced agent build time; improved debugging via tracing/auditability)
- Designs reliable LLM systems using hard constraints (JSON-schema tool validation, deterministic controllers, policy enforcement)
- Expertise in multi-step agent workflows with durable state, safe retries, and idempotency
- Strategic use of orchestration frameworks (LangChain for linear flows; LangGraph for branching/state-machine workflows)
- Evaluation-driven development: regression suites from real production failures plus telemetry-based reliability tracking
- Strong cross-functional delivery with non-technical legal/finance stakeholders by constraining AI to interpretation/classification and keeping calculations deterministic
- Turns LLM prototypes into production-ready systems via workflow mapping, schemas, grounding, and guardrails
- Strong observability-driven debugging (tracing, structured logs, metrics) to isolate failures quickly
- Prevents harmful downstream actions with validation gates, deterministic checks, and safe fallback paths
- Rapid iteration using real production data; converts incidents into regression tests and alerts
- Diagnoses subtle retrieval degradation and fixes with filters, re-ranking, and required-fact assertions
- Effective technical communication through credible demos/workshops that include constraints and failure modes
- Bridges sales/customer needs and engineering reality; designs risk-focused PoCs that build trust and drive adoption
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