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 Responsibilities
• Develop and optimize AI systems leveraging LLMs, RAG, and agentic AI frameworks (LangChain, LangGraph).
• 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.
Required Skills & Experience
• Strong hands-on experience with LLMs, RAG, and agentic frameworks (LangChain, LangGraph, Semantic Kernel, etc.).
• Proficiency in Python, with deep understanding of ML libraries like PyTorch, TensorFlow, 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.
Preferred Skills
• 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.
Soft Skills
• Strong analytical and problem-solving mindset.
• Excellent collaboration and communication skills.
• Passion for innovation, experimentation, and applied AI.
