Role & responsibilities • Own problems end-to-end: Translate business goals into technical plans; design pragmatic solutions; deliver production systems with measurable impact. • Build production back ends: Design and implement APIs and microservices (REST/gRPC) for GenAI workloads; containerize and orchestrate services (Docker/Kubernetes/ECS/EKS). • Ship on AWS: Leverage AWS (e.g., Lambda, ECS/EKS, S3, DynamoDB/RDS, API Gateway, SQS/SNS, CloudWatch) plus AI services (e.g., Bedrock, SageMaker) to train, host, and integrate models. • Work across modalities: Deliver features for text (LLMs/RAG) and images (VLMs/CV) including retrieval, embeddings, fine-tuning/adapters, and evaluation pipelines. • Make it observable: Instrument logging, metrics, and traces (OpenTelemetry/CloudWatch/Datadog/etc.); build dashboards, SLOs/SLIs, and alerts; own performance, reliability, and cost. • Validate and govern: Implement offline/online evaluations, A/B tests, guardrails/red-teaming, data and model quality checks, and safety/compliance gates. • Automate the path to prod: Establish CI/CD (GitHub Actions/CodePipeline), infrastructure as code (Terraform/CloudFormation), automated tests, and rollouts (canary/blue-green). • Collaborate without handoffs: Partner with product, domain experts, and downstream teams; document architecture; support launches; close the loop with data-driven iteration. Preferred candidate profile • At least one year owning a production GenAI or ML system (not a side project), plus 3 5+ years total professional experience building back-end or ML-powered products. • Services you built that are running in production with users/traffic, clear SLIs/SLOs, and release/incident history. • Evidence of quality: eval frameworks, regression tests, canary strategies, monitoring dashboards, cost/perf optimizations you introduced. Required Experience • GenAI foundation: LLMs/VLMs, embeddings, RAG, prompt orchestration, adapters/fine-tuning, tokenization, latency/cost trade-offs, content safety/guardrails. • Back-end & systems: Strong design of microservices, APIs, event-driven patterns; data modeling across SQL/NoSQL; familiarity with vector databases. • AWS & cloud infra: IAM/KMS/secrets, networking, containers/orchestration, CI/CD, IaC; operating services in AWS with cost/performance ownership. • Observability & reliability: Logging, metrics, traces; performance profiling; incident response; chaos and load testing; availability and scaling strategies. • Languages & tooling: Proficient in Python (plus one of TypeScript/Go/Java); PyTorch/TensorFlow; Docker/Kubernetes; git; testing frameworks. How Well Collaborate • This is a hands-on IC role — not people management. You’ll partner closely with product and customers and will be expected to roll up your sleeves daily. • Title is flexible (e.g., AI Systems Engineer, AI Product Engineer, Senior Software Engineer — AI, ML Engineer (Production)). We care about what you’ve built and shipped, not the label. Minimum Qualifications • Bachelor’s degree in CS/EE or equivalent practical experience. • 3–5+ years in software/ML engineering with 1 year owning production AI/ML systems. Role: Technical Architect,Industry Type: BPM / BPO,Department: Engineering - Software & QA,Employment Type: Full Time, PermanentRole Category: Software DevelopmentEducationUG: Any Graduate