Machine Learning / AI Engineer

Position Overview We are hiring a Machine Learning / AI Engineer with 3–6 years of experience designing and deploying AI systems in production environments. This role requires strong Python expertise, hands-on experience with generative AI architectures, and the ability to productionize LLM driven systems with reliability, observability, and cost efficiency in mind. The ideal candidate can bridge research and engineering, translating AI concepts into scalable systems. Key Responsibilities • Design and deploy Retrieval-Augmented Generation (RAG) pipelines using embedding models and vector databases such as FAISS, Pinecone, Weaviate, or Chroma. • Develop LLM-powered applications using frameworks such as LangChain, LlamaIndex, AutoGen, or similar orchestration frameworks. • Implement agent-based systems that support multi-step reasoning, tool invocation, and structured workflows. • Fine-tune or adapt models using techniques such as LoRA (Low-Rank Adaptation) and parameter-efficient fine-tuning (PEFT). • Build structured output pipelines using schema validation and function-calling APIs. • Develop automated evaluation pipelines, including benchmarking datasets, regression testing for prompts, and output quality scoring. • Monitor and optimize latency, token usage, throughput, and cost across AI workflows. • Build observability layers for AI systems including logging, tracing, prompt telemetry, and output validation. • Collaborate with backend and frontend teams to integrate AI services into production systems via REST APIs or asynchronous workflows. • Ensure robustness against hallucinations, prompt injection, and adversarial inputs through validation logic and contextual constraints. Required Technical Skills • Advanced proficiency in Python. • Hands-on experience with PyTorch and/or TensorFlow. • Practical experience with Hugging Face Transformers and modern LLM APIs. • Strong understanding of tokenization, embeddings, semantic similarity, and vector search. • Experience building and optimizing RAG pipelines. • Familiarity with containerization (Docker) and deployment in Kubernetes environments. • Experience with MLOps tooling for model versioning, deployment, and monitoring. • Strong API design and microservices fundamentals. Preferred Qualifications • Experience deploying open-source models in production environments. • Experience with distributed training or inference optimization. • Familiarity with cloud ML platforms (AWS SageMaker, GCP Vertex AI, Azure ML). • Knowledge of evaluation frameworks and human-in-the-loop validation systems. Ideal Candidate Profile • Execution-oriented with a strong focus on production readiness. • Able to reason deeply about model behavior, trade-offs, and system constraints. • Comfortable iterating quickly while maintaining engineering rigor. • Collaborative and able to clearly explain complex AI systems to cross-functional teams. • Driven by measurable product impact rather than experimentation alone.