Forward-deployed AI Engineer
monish gosar, AI Engineer
I design and ship production agentic systems end-to-end, from discovery to multi-agent architecture, serving, and evaluation.
About the AI Engineer
hey, i'm monish. i'm a forward-deployed AI engineer and data scientist who designs and ships production agentic systems end-to-end, from discovery to multi-agent architecture, serving, and evaluation.
right now i'm focused on agentic systems that actually understand the domain they operate in — purpose-built skills, grounded tool calls, and the ability to run full multi-turn analysis without losing context or making things up. My background in data science helps me ensure that every AI workflow is backed by robust, clean data.
AI Engineering Experience
Indilabs.ai
Forward-deployed AI Engineer
- —Embedded with operating teams to translate ambiguous, manual workflows into agentic systems: running discovery, designing the architecture, and driving adoption into daily production use.
- —Designed a co-analyst framework that turns recurring analytical workflows into reusable skill cards: fetching live data, running domain analyses, and producing structured, decision-ready outputs.
- —Architected a multi-agent analytics platform on Azure OpenAI + OpenAI Agents SDK with schema-constrained tool calling and live data services: auditable answers in under 15 seconds, cutting reporting effort 40%.
- —Owned the LLM serving layer: token-budget management, rate limiting, latency reduction; plus a production ML inference layer (delinquency, propensity, forecasting) behind low-latency REST APIs across 21+ products.
- —Built a custom agent runtime with parallel tool execution, structured outputs, Langfuse tracing, and LLM-as-a-judge evaluation for factuality, completeness, and bias.
- —Shipped a contactless settlement product with NPV/DCF-optimized offers, propensity scoring, and an AI negotiation layer: +12% settlement acceptance.
Azure OpenAIOpenAI Agents SDKLangfuseFastAPIMachine Learning
RE Journal
Data Analyst
- —Segmented 1,000+ users into 3 behavioral clusters via K-Means across 6 features for buyer profiling insights.
- —Built analytics pipeline: scraped 150K+ RERA records (Selenium, BeautifulSoup), modeled SQL database, delivered Power BI & Streamlit dashboards.
PythonSeleniumSQLPower BIStreamlit