SSI is expanding its AI capabilities and is looking for a hands-on AI/ML Engineer who brings depth in both model development and production operations. This is a high-impact individual contributor role: you will own the end-to-end ML lifecycle — from experimentation and fine-tuning LLMs to deploying and monitoring models in production.
KEY RESPONSIBILITIES
• Design, fine-tune, and evaluate large language models (LLMs) for domain-specific applications.
• Build and maintain production-grade MLOps pipelines — training, versioning, deployment, monitoring.
• Implement RAG (Retrieval-Augmented Generation) architectures and agentic LLM workflows.
• Own model serving infrastructure using tools such as TorchServe, vLLM, TGI, or Triton.
• Set up and manage experiment tracking (MLflow, W&B) and model registries.
• Develop evaluation frameworks to rigorously benchmark model quality, latency, and safety.
• Collaborate with product and engineering teams to translate AI capabilities into product features.
• Monitor model drift, performance degradation, and data quality in live systems.
• Document and communicate model design decisions, trade-offs, and results clearly.
REQUIRED QUALIFICATIONS
• 5+ years of experience in machine learning engineering or applied AI roles.
• Hands-on experience fine-tuning or building applications with LLMs (GPT, LLaMA, Mistral, Gemini, Claude, etc.).
• Strong Python skills; proficiency with PyTorch or JAX for model development.
• Production MLOps experience: CI/CD for ML, containerization (Docker/Kubernetes), cloud ML platforms (AWS SageMaker, GCP Vertex AI, or Azure ML).
• Experience with vector databases (Pinecone, Weaviate, pgvector, Qdrant) and embedding pipelines.
• Solid understanding of NLP fundamentals: tokenization, attention, RLHF, PEFT/LoRA, quantization.
• Experience with prompt engineering, prompt chaining, and LLM evaluation techniques.
• Ability to independently architect AI systems and deliver production releases without close supervision.
NICE TO HAVE
• Experience with multi-modal models (vision-language, speech-to-text, text-to-image).
• Familiarity with LLM frameworks: LangChain, LlamaIndex, DSPy, or Semantic Kernel.
• Knowledge of AI safety, guardrails, and responsible AI deployment practices.
• Published research or recognized open-source contributions in ML/NLP.
• Experience building AI-powered products in fintech, healthtech, or enterprise SaaS domains.