Senior AI/ML Engineer

Senior AI/ML Engineer (11+ Years of Experience)

Employment Type: Contract W2 only
Experience: 11+ years total, with 6+ years in applied ML/AI

About the Role

We are looking for a Senior AI/ML Engineer to design, build, and ship production-grade machine learning and generative AI systems. You will own problems end to end, from framing the business question and shaping data pipelines to training, deploying, monitoring, and iterating on models at scale. You will also act as a technical leader, setting standards, mentoring engineers, and influencing the AI roadmap.

Key Responsibilities

Model Development & Applied Research

• Design, train, and optimize ML and deep learning models for problems such as NLP, recommendation, forecasting, computer vision, and anomaly detection
• Build and productionize LLM-based solutions: RAG pipelines, fine-tuning (LoRA/PEFT), prompt engineering, agents, and evaluation frameworks
• Evaluate new research and techniques, and decide pragmatically what is worth adopting

MLOps & Production Engineering

• Build scalable training and inference pipelines with CI/CD, model versioning, and automated retraining
• Deploy models as low-latency, high-availability services (REST/gRPC, batch, and streaming)
• Implement monitoring for data drift, model drift, latency, cost, and quality
• Optimize inference performance and cloud spend (quantization, batching, caching, GPU utilization)

Data & Architecture

• Partner with data engineering to design feature stores, data pipelines, and training datasets
• Define architecture for ML platforms and reusable components across teams
• Ensure data quality, lineage, privacy, and governance

Leadership & Collaboration

• Lead technical design reviews and code reviews; set best practices for ML engineering
• Mentor junior and mid-level engineers and data scientists
• Work with product, engineering, and business stakeholders to translate requirements into measurable ML outcomes
• Communicate trade-offs, risks, and results clearly to technical and non-technical audiences

Responsible AI

• Apply practices for fairness, explainability, security, and compliance
• Build guardrails and evaluation for LLM safety, hallucination, and bias

Required Qualifications

• 11+ years of software/data engineering experience, including 6+ years building and deploying ML systems in production
• Bachelor's or Master's in Computer Science, AI/ML, Statistics, or a related field (PhD a plus)
• Expert-level Python; strong software engineering fundamentals (data structures, testing, design patterns, code quality)
• Deep hands-on experience with PyTorch and/or TensorFlow, plus scikit-learn, XGBoost/LightGBM
• Proven experience with LLMs and generative AI: Hugging Face, LangChain/LlamaIndex, vector databases (Pinecone, FAISS, Weaviate, pgvector), RAG, fine-tuning
• Strong foundation in statistics, probability, optimization, and experiment design (A/B testing)
• Experience with MLOps tools: MLflow, Kubeflow, Airflow, SageMaker/Vertex AI/Azure ML, Docker, Kubernetes
• Hands-on cloud experience (AWS, Google Cloud Platform, or Azure)
• Experience with big data tools: Spark, Kafka, SQL/NoSQL, data warehouses
• Track record of delivering ML solutions with measurable business impact
• Strong communication and technical leadership skills

Preferred Qualifications

• Experience with distributed training and serving (Ray, DeepSpeed, vLLM, Triton)
• Knowledge of agentic workflows, tool use, and multi-agent orchestration
• Experience with real-time ML systems and feature stores (Feast, Tecton)
• Familiarity with model optimization (ONNX, TensorRT, quantization)
• Domain experience in [fintech / healthcare / e-commerce / etc.]
• Publications, patents, open-source contributions, or conference talks
• Experience with Infrastructure as Code (Terraform) and observability tools