AI/ML Engineer & Forward Deployed Engineer (FDE)
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I'm an AI/ML Engineer and Forward Deployed Engineer with 4+ years of experience building production Generative AI systems — but the thread that runs through all of it is a genuine obsession with making AI actually work in the real world, not just in demos.
I started my career as a .NET developer at Tata Consultancy Services, where I built enterprise applications and automated document processing pipelines for a large insurance client. That foundation — understanding how enterprises actually store, move, and trust data — turned out to be invaluable when I pivoted fully into AI/ML. I didn't just learn the models; I learned the messy reality they have to operate in.
Today I work as a Forward Deployed Engineer at Solix Technologies, embedded with enterprise clients to take AI from proof-of-concept to production. I've shipped RAG pipelines, multi-agent systems, Text-to-SQL engines, and evaluation frameworks across industries — and I've learned that the hardest part is rarely the model. It's trust, governance, latency, and adoption.
Outside of work, I'm the kind of person who reads AI safety research for fun and gets genuinely excited about papers on interpretability and agent interoperability. The field is moving fast enough that staying curious isn't optional — it's the job.
What I'm looking for now is an environment where the technical bar is high, the mission is serious, and the work has real stakes. Anthropic fits that description better than anywhere else I can imagine. I want to build AI systems that are not just capable, but trustworthy — and I want to do it alongside people who care about that distinction as much as I do.
Here's a polished, interview-ready walkthrough of your work experience:
AI/ML Engineer & Forward Deployed Engineer — Solix Technologies Inc. (Oct 2024 – Present)
This is where my work converged fully into production Generative AI. As an FDE, I'm embedded with enterprise clients end-to-end — from scoping and architecture through to deployment and adoption. Across 3 enterprise accounts, I've driven full AI adoption within 6 months of engagement start.
The technical scope has been broad: architecting RAG pipelines ingesting 50,000+ documents per client with hybrid search and re-ranking, cutting retrieval latency 35% and improving relevance 40%. Building multi-agent systems with LangGraph and CrewAI integrating 6+ enterprise APIs, reducing manual workflow effort 60% at 95%+ task accuracy. Implementing MCP servers to standardize tool connectivity across agentic systems, cutting integration development time 40%.
Shipping 10+ FastAPI microservices with streaming SSE responses serving up to 2,000 daily requests at 99.5%+ uptime.
Beyond engineering, I've led 15+ solution design workshops, authored FDE playbooks adopted by 2 teams, and contributed to 3 contract renewals through technical demos and client trust-building.
DevOps Engineer — Origin Hubs Inc. (Jul 2024 – Oct 2024)
A focused bridge role where I built CI/CD pipelines with Jenkins and GitHub Actions — cutting deployment time from 45 to 12 minutes — and deployed a Grafana/Prometheus observability stack that reduced mean time to detection from 22 to 8 minutes. This deepened my instinct for infrastructure reliability and monitoring, which directly informs how I instrument AI systems today..NET Developer — Tata Consultancy Services (Protective Life Insurance) (Oct 2019 – Aug 2022)
This is where I built my enterprise engineering foundation. I developed scalable ASP.NET MVC applications supporting 500,000+ insurance policy records at 99.9% uptime, automated 3 OCR and document processing pipelines eliminating 1,200+ hours of manual work annually, and optimized SQL Server performance across 5 reporting modules — cutting execution time 40%.
What this role gave me that pure AI experience doesn't is a deep understanding of how large enterprises actually operate: their data complexity, compliance requirements, risk aversion, and the organizational inertia that AI systems have to work within. That context has made me a significantly more effective FDE.
The through-line across all three roles is moving from building reliable enterprise software → instrumenting reliable infrastructure → deploying reliable AI. Each layer informs the next, and together they're why I think about production AI the way I do — not just as a modeling problem, but as a systems and trust problem.
MS in Computer Science — Western Illinois University (Graduated May 2024, GPA: 3.8/4.0)
My master's is where I made the deliberate pivot into AI/ML. I focused my coursework and projects around machine learning, NLP, and distributed systems — building the theoretical foundation that now underpins how I approach production AI architecture. The 3.8 GPA reflects genuine engagement with the material, not just credential-chasing.
Completing the degree while transitioning into industry AI work meant I was constantly pressure-testing classroom concepts against real implementation challenges — which I think gave me a more grounded understanding than either path alone would have.
BTech in Computer Science — Jawaharlal Nehru Technological University (Graduated May 2019, GPA: 3.4/4.0)
My undergraduate degree gave me the core CS fundamentals — algorithms, data structures, software engineering principles, and systems design — that I still draw on daily. It also set me up well for the enterprise software engineering work I did early in my career at TCS.
What I'd add beyond the degrees:
Formal education aside, I treat continuous learning as a professional obligation in this field — the pace of change makes it non-negotiable. I've completed DeepLearning.AI specializations in LangChain for LLM Application Development, Building Systems with the ChatGPT API, and LLM Evaluation & Monitoring. I'm currently pursuing AWS Certified Machine Learning Specialty, Microsoft Azure AI Engineer (AI-102), and the DeepLearning.AI LLMOps Specialization.
The certifications matter less to me than what they represent — a habit of staying current as the field evolves underneath us.
