Lead AI/ML Engineer
Lead AI/ML Engineer
TEKsystems10-12 Years
- Posted 4 hours ago
- Be among the first 10 applicants
Job Description
Required Technical Skills / Experience
- 10+ years of professional software engineering experience, with 2+ years building GenAI systems that ran in production.
- Has built and operated an agentic system in production — multi-step, tool-calling, used by real users — including the reliability work: retries, loop detection, timeouts, and fallbacks.
- Has personally built a RAG pipeline end to end — ingestion, embedding, retrieval, generation — and can explain the reasoning behind each design decision, not only the tools used.
- Demonstrated evaluation practice: has built eval sets and can quantify how output quality was measured and how it moved.
- Accountable for a production system after go-live — deployment, monitoring, cost, and incident response.
- AWS depth — Bedrock, S3, Lambda, OpenSearch or a comparable vector store, IAM and Secrets Manager.
- Strong Python, with solid API design and integration experience against existing enterprise services.
- Demonstrably hands-on within the last 18 months. This is a building role, not an oversight role.
More Info
Key Skills
generation
Secrets Manager
evaluation practice
GenAI systems
vector store
embedding
retrieval
multi-step tool-calling
agentic system
RAG pipeline
ingestion
About Company
Skills:
Retrieval-Augmented Generation (RAG), Tensorflow, Pytorch, Gcp, Data Warehousing, Azure, Python, AWS, LangChain, Generative AI, LLMs, Hugging Face Transformers, ETL/ELT Tools, LlamaIndex
Skills:
Retrieval-Augmented Generation (RAG, Python, C#, C++, Numpy, Pandas, MLops, Etl, Pytorch, Orchestration, AI coding assistants, Evaluation, Agentic AI solutions, OpenAI-compatible APIs, LLMOps, Azure OpenAI, Data pipelines, LangChain, Embeddings, Vector search, Tool calling, Anthropic Claude, LangGraph
Skills:
Apis, Microservices, system integration, MLops, Microsoft Azure, AWS, cloud-native solution development, continuous integration for AI models, AWS Developer CLI, Agentic AI solutions, GitHub Copilot, AI-assisted coding tools, vibe coding environments
Skills:
containerisation with Docker, secrets management, pipeline automation with Azure DevOps, networking and load balancing, relational and non-relational databases, developing production-grade backend services using Python, asynchronous programming frameworks, cloud provisioning frameworks such as Terraform or CloudFormation, infrastructure-as-code, LLM API gateway technologies, container orchestration such as ECS Fargate, identity and access management technologies, observability and monitoring tools, multi-provider integration across major cloud AI platforms
Skills:
Keras, Opencl, Java, Tensorflow, Django, Rust, Pytorch, Cuda, FastAPI, Kubernetes, Python, Docker, Flask, Azure ML, Go, ONNX, AWS Sagemaker, DeepStream, Caffe, TensorRT