Forward Deployed Engineer - AI - LLM

keka

Bangalore 3 Years Exp Posted 47d ago

Embed with customers. Work directly with engineering, product, and business teams to understand workflows, data, and the outcomes they need.
Translate ambiguity into design. Decompose business problems into AI and agentic system designs grounded in evals, latency budgets, and cost envelopes.
Own the technical narrative. Run architecture reviews and workshops with C-suite, IT, and security stakeholders.
Architect on AWS. Build production systems on Bedrock, SageMaker, Lambda, and EKS, using Aivar's accelerators as the foundation.
Build agentic systems. Develop LLM workflows — prompt engineering, RAG, tool use, multi-agent orchestration, evaluation harnesses, and guardrails.
Integrate deeply. Connect with customer systems of record — ERPs, data warehouses, CRMs, contact-center platforms, and legacy APIs.
Ship to production. Take systems from prototype to hardened deployment — observability, SLOs, cost optimization, security reviews, and runbooks.
Drive adoption. Define success metrics with the customer, instrument systems to measure them, and iterate until the numbers move.
Enable customer teams. Lead change management and training so customers can extend what you build.
Feed the product. Codify repeatable patterns into the accelerator roadmap and partner with Product and Engineering on what becomes platform vs. bespoke.

4+ years of software engineering experience, including 2+ years in customer-facing or production-deployment roles (FDE, Solutions Engineer, Applied AI Engineer, or technical founder).
Strong coding fluency in Python and at least one of TypeScript/JavaScript, Java, or Go. You ship production code.
Hands-on production experience with LLMs — prompt engineering, RAG, agent design, evaluation frameworks, and scaled deployment.
Working knowledge of AWS for AI workloads — Bedrock, Lambda, S3, IAM, ECS/EKS, and at least one data service (RDS, DynamoDB, or OpenSearch).
Solid ML foundations — evaluation methodology, problem decomposition, and reasoning about model behavior in production.
Experience integrating across enterprise systems — REST/GraphQL, event-driven architectures, and at least one of: ERP, data warehouse, CRM, or contact-center platforms.
Strong written and verbal communication. You move comfortably between a CTO working session and a platform engineering code review.
Comfort with ambiguity, ownership of outcomes, and a bias to ship.

Production work with agentic frameworks (LangGraph, LlamaIndex, CrewAI, Bedrock Agents, or equivalents).
Kubernetes and cloud-native deployment — Helm, GitOps, Prometheus/Grafana/OpenTelemetry.
Voice AI and contact-center stacks (ASR/TTS, telephony, real-time streaming) — relevant to Convogent.
Regulated-industry experience (fintech, healthcare) and familiarity with SOC 2, HIPAA, RBI/SEBI, or HITRUST.
Prior FDE, Applied AI, or Solutions Architect roles at a top AI company, hyperscaler, or high-growth B2B startup.
AWS certifications (Solutions Architect, ML Specialty) or equivalent depth.
Track record of shaping platform or product roadmaps from field learnings.

Work that ships. Production Agentic AI for enterprise customers who measure outcomes, not demos.
Backed and proven. AI-native, AWS Preferred Partner, BVP- and Sorin-backed, 100+ enterprise customers in the first year.
End-to-end ownership. Lead engagements without layers of management. Decisions and code live with you.
Accelerators that work. Build on top of Convogent, Velogent, Kubogent,— not from zero.
Shape the product. Direct collaboration with founders, product, and the AI engineering bench. Your field signal drives the roadmap.
Compensation. Competitive base, performance bonus, AWS/AI learning budget, and conference sponsorship.