Senior Machine Learning Engineer

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