Senior AI/ML Engineer

Job Title: Senior AI/ML Engineer

Experience: 10+ Years

Employment Type: W2 only

Job Description

We are seeking a highly experienced Senior AI/ML Engineer with 10+ years of experience designing, building, and deploying machine learning and AI systems at scale. The ideal candidate has deep expertise across the ML lifecycle — from data engineering and model development to production deployment and MLOps — and can lead technical strategy while mentoring junior engineers.

Key Responsibilities

• Design, develop, and deploy machine learning models and AI systems for large-scale production environments
• Lead end-to-end ML lifecycle: data collection, feature engineering, model training, validation, deployment, and monitoring
• Architect scalable ML pipelines using MLOps best practices (CI/CD for ML, model versioning, automated retraining)
• Work with deep learning frameworks to build NLP, computer vision, or recommendation systems as needed
• Collaborate with data engineers, product managers, and business stakeholders to translate requirements into ML solutions
• Optimize models for performance, latency, and cost in production (quantization, distillation, inference optimization)
• Evaluate and integrate LLMs / Generative AI solutions where applicable (RAG pipelines, fine-tuning, prompt engineering)
• Establish best practices for model governance, explainability, and responsible AI
• Mentor junior/mid-level engineers and conduct code/design reviews
• Stay current with emerging AI/ML research and evaluate applicability to business problems

Required Skills & Experience

• 10+ years of overall software/ML engineering experience, with 6+ years focused specifically on ML/AI
• Strong programming skills in Python (R or Scala a plus)
• Expertise in ML frameworks: TensorFlow, PyTorch, Scikit-learn, Keras
• Hands-on experience with MLOps tools: MLflow, Kubeflow, SageMaker, Vertex AI, or Azure ML
• Strong background in data engineering (SQL, Spark, Airflow, ETL pipelines)
• Experience with cloud platforms: AWS, Azure, or Google Cloud Platform (certifications a plus)
• Proficiency with containerization/orchestration: Docker, Kubernetes
• Experience with LLMs, Generative AI, and vector databases (e.g., Pinecone, FAISS, Weaviate) is highly desirable
• Solid understanding of statistics, algorithms, and deep learning architectures (CNNs, RNNs, Transformers)
• Experience with model deployment via REST APIs, gRPC, or serverless architectures
• Strong communication skills and experience presenting to technical and non-technical stakeholders