Machine Learning Engineer – Generative AI; LLMs/RAG/Agentic AI
Role Summary
Stellar Technologies is seeking a Machine Learning Engineer (GenAI) to design, build, and deploy next‑generation AI systems combining Large Language Models (LLMs),
Retrieval‑Augmented Generation (RAG), and agentic AI frameworks
.
In this role, you will bridge model development and production engineering — developing scalable AI pipelines, integrating real‑time APIs, and ensuring high‑performance AI services that power enterprise‑grade solutions. You will work at the intersection of machine learning, cloud infrastructure, and applied research, collaborating with top engineers and data scientists to deliver intelligent, production‑ready AI capabilities.
Key ResponsibilitiesDevelop and optimize AI systems leveraging LLMs, RAG, and agentic AI frameworks (Lang Chain, Lang Graph).
Build and deploy production‑grade ML pipelines with real‑time inference and retrieval components.
Design and manage APIs and streaming services to integrate AI models into enterprise platforms.
Implement containerized, orchestrated deployments using Docker, Kubernetes, and Azure ML
.Automate data preprocessing, model training, evaluation, and versioning pipelines.
Collaborate with cross‑functional teams to integrate models into front‑end, analytics, and automation workflows.
Ensure governance, compliance, and security of deployed AI workloads.
Conduct performance benchmarking and optimize inference latency and cost.
Monitor AI systems in production using observability frameworks (logging, metrics, tracing).
Participate in architecture discussions to enhance scalability and reliability of AI services.
Strong hands‑on experience with LLMs, RAG, and agentic frameworks (Lang Chain, Lang Graph, Semantic Kernel, etc.).
Proficiency in Python
, with deep understanding of ML libraries like PyTorch, Tensor Flow, scikit‑learn, Hugging Face Transformers
.Solid experience in API and microservices engineering (FastAPI, Flask).
Familiarity with streaming architectures and real‑time data handling.
Knowledge of cloud platforms (Azure preferred), including Azure AI, Cognitive Services, and ML Ops.
Experience with
containerization and orchestration (Docker, Kubernetes).Understanding of vector databases (Pinecone, Weaviate, FAISS) and retrieval mechanisms.
Experience in CI/CD, model deployment, and production monitoring.
Exposure to GPU‑based inference optimization and serverless deployment
.Knowledge of observability and monitoring tools for AI (Prometheus, Grafana, Azure Monitor).
Experience in model fine‑tuning
, prompt engineering
, or agentic orchestration
.Understanding of AI governance, ethical AI, and data privacy frameworks.
Strong analytical and problem‑solving mindset.
Excellent collaboration and communication skills.
Passion for innovation, experimentation, and applied AI.
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