Senior Machine Learning Engineer
Build and productionize large-scale recommendation systems, NLP/embedding models, and agentic AI workflows. Requires 6+ years ML/NLP experience and expertise in PyTorch/TensorFlow, RAG, and vector search.
About the job
What you'll do
Recommendation system
- Build large scale recommendation systems utilizing embeddings generated for structured and unstructured data using methods such as a two tower architecture
- Performant recommendation designs which can scale to millions of recommendations per day for different product features
- Utilize graph based structures, search and scoring to enhance recommendation quality
Advanced NLP & Embedding Systems
- Fine-tune (LORA/PEFT), customize and deploy embedding models (LLMs/SLMs) for multi-language text understanding and semantic search
- Architect vector search solutions that enable language-agnostic clustering and classification across global datasets
- Build and optimize high-performance retrieval systems using vector databases
MLOps Lifecycle Management
- Architect and manage scalable MLOps and LLMOps infrastructure for robust model training, evaluation, deployment, and monitoring systems
- Design comprehensive CI/CD pipelines, implement model monitoring frameworks to identify drift patterns, and ensure high availability and fault tolerance
- Help establish metrics, experimentation frameworks, and statistical validation approaches for AI system performance
Agentic Workflows & Evaluation
- Design and implement agentic systems for automated web extraction, NER, and entity resolution tasks
- Build comprehensive evaluation frameworks for agent performance across data acquisition and processing workflows
- Create feedback loops that continuously improve agent decision-making and data quality outcomes
- Build, and scale MCP servers and integrate them into broader AI and product ecosystems
Cross-Functional Collaboration
- End to end ownership of production workflows with close collaboration across engineering teams managing data, application, API and MCP layers to ensure models integrate seamlessly and scale with business needs
- Work with Product Management to translate business requirements into scalable ML solutions
What you bring
- 6+ years hands-on ML/NLP experience (or 3+ years post-PhD/Master's) with at least two delivered, revenue-impacting products in production environments
- Expertise in modern AI architectures including transformer stacks, prompt engineering, RAG systems, vector-based information retrieval and context engineering
- Proven track record building and managing production systems by architecting and deploying scalable distributed systems of REST & MCP based microservices for applications and agents with observability and monitoring of latency, token utilization and system reliability
- Strong applied research capabilities (PyTorch or TensorFlow) paired with software-engineering rigor (Python) and familiarity with open weight LLMs (QWEN, Gemma, OSS) and embedding models and vector search technologies (FAISS, Pinecone)
- Executive communication skills with ability to persuade technical and non-technical audiences through data-driven storytelling, comfortable owning strategy, budget, and cross-functional collaboration
- Utilize modern AI development tools (Claude Code, Codex, Cursor) in their engineering workflow to maximize development velocity and code quality
Skills
PyTorch, TensorFlow, Python, RAG, Faiss, Pinecone, Transformers, Lora, Peft, LLMs
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