Senior AI/ML Engineer

Senior AI/ML Engineer – Full-Time

Location: United States

Employment Type: Full-Time

Experience: 10+ Years

Work Authorization: US Work Authorization Required

Job Summary

We are seeking an experienced Senior AI/ML Engineer with 10+ years of experience designing, developing, and deploying scalable machine learning and AI solutions. The ideal candidate will have strong expertise in Python, machine learning, deep learning, Generative AI, LLMs, NLP, cloud platforms, MLOps, and production-grade AI systems .

Key Responsibilities

• Design, develop, and productionize machine learning and AI solutions for complex business problems.
• Build and optimize ML models, deep learning architectures, NLP solutions, and Generative AI applications .
• Develop LLM-powered applications using techniques such as prompt engineering, RAG, embeddings, vector search, fine-tuning, and evaluation .
• Work with OpenAI, Azure OpenAI, AWS Bedrock, Hugging Face, LangChain, and LlamaIndex or comparable AI platforms.
• Develop scalable data and feature pipelines using Python, SQL, Spark, and PySpark .
• Implement and optimize models using TensorFlow, PyTorch, Scikit-learn, XGBoost , and other ML frameworks.
• Deploy AI/ML models using AWS, Azure, or GCP cloud services.
• Build MLOps pipelines for model training, validation, deployment, monitoring, versioning, and retraining.
• Implement model serving using Docker, Kubernetes, REST APIs, FastAPI , and cloud-native technologies.
• Work with MLflow, Kubeflow, Azure ML, SageMaker , or equivalent MLOps platforms.
• Develop AI solutions involving NLP, recommendation systems, classification, forecasting, anomaly detection, and predictive analytics .
• Implement vector databases such as Pinecone, FAISS, Weaviate, Milvus, or Azure AI Search.
• Establish model evaluation, observability, performance monitoring, and responsible AI practices.
• Collaborate with data engineers, software engineers, architects, product teams, and business stakeholders.
• Mentor junior and mid-level engineers and provide technical leadership across AI/ML initiatives.

Required Technical Skills

• 10+ years of experience in AI/ML, machine learning engineering, data science, or related fields.
• Strong programming experience with Python and SQL .
• Strong knowledge of Machine Learning, Deep Learning, NLP, and Generative AI .
• Hands-on experience with LLMs, RAG, prompt engineering, embeddings, vector databases, and model evaluation .
• Experience with PyTorch, TensorFlow, Scikit-learn, XGBoost or similar frameworks.
• Strong understanding of Transformers, neural networks, model training, fine-tuning, and inference optimization .
• Experience with at least one major cloud platform: AWS, Azure, or GCP .
• Strong knowledge of MLOps, CI/CD, model lifecycle management, and production deployment .
• Experience with Docker, Kubernetes, Git, REST APIs, and microservices .
• Experience with Spark/PySpark and large-scale data processing.
• Strong understanding of data structures, algorithms, statistics, probability, and model evaluation techniques .

Preferred Skills

• Experience with Azure OpenAI, AWS Bedrock, Amazon SageMaker, Azure Machine Learning, or Vertex AI .
• Experience building Agentic AI / AI Agent solutions.
• Knowledge of LangChain, LlamaIndex, Semantic Kernel , or comparable frameworks.
• Experience with RAG architecture, hybrid search, reranking, and knowledge graphs .
• Experience with LLM fine-tuning, LoRA/PEFT, quantization, and inference optimization .
• Knowledge of Kafka, Airflow, Databricks, Snowflake, or Delta Lake .
• Experience implementing AI governance, security, privacy, explainability, and responsible AI .
• Strong communication, architecture, problem-solving, and technical leadership skills.

Education

Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, Engineering, Mathematics , or a related technical discipline.

Ideal Candidate Profile

The ideal candidate should be capable of taking an AI/ML initiative from problem definition → data preparation → model development → experimentation → deployment → monitoring → continuous optimization , while working closely with engineering and busine…