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.

• Develop 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.