Senior AI/ML Engineer

Job Title: Senior / Lead AI/ML Engineer
Experience: 11+ Years
Employment Type: W2 Contract
Role Level: Senior / Lead / Architect

Job Summary

We are looking for an experienced Senior AI/ML Engineer with 11+ years of experience to design, develop, deploy, and scale enterprise-grade Artificial Intelligence and Machine Learning solutions. The ideal candidate will have strong expertise in Python, Machine Learning, Deep Learning, Generative AI, LLMs, NLP, MLOps, cloud platforms, and scalable AI architecture.

The role requires a strong combination of software engineering, machine learning, cloud, and production deployment experience. The engineer will work closely with data scientists, software engineers, product teams, and business stakeholders to convert complex business requirements into reliable and scalable AI solutions.

Modern senior AI/ML roles increasingly emphasize productionization, cloud infrastructure, Kubernetes/Docker, PyTorch/TensorFlow, and MLOps in addition to model development.

Key Responsibilities

• Lead the design and development of end-to-end AI/ML solutions from data preparation and experimentation through production deployment.

• Develop and optimize supervised, unsupervised, and deep learning models.

• Build solutions using Python, Scikit-learn, PyTorch, TensorFlow, Keras, and related ML frameworks.

• Design and implement Generative AI and LLM-based applications, including RAG, prompt engineering, embeddings, vector search, and AI agents.

• Develop NLP, text classification, recommendation, forecasting, anomaly detection, and predictive analytics solutions.

• Design scalable ML pipelines and MLOps workflows for model training, validation, deployment, monitoring, and retraining.

• Implement model versioning, experiment tracking, feature management, model registries, and automated model deployment.

• Deploy ML models using Docker, Kubernetes, REST APIs, FastAPI, and cloud-native services.

• Build CI/CD and continuous training pipelines using tools such as GitHub Actions, Jenkins, GitLab CI, or Azure DevOps.

• Work with MLflow, Kubeflow, Airflow, Databricks, AWS SageMaker, Azure ML, or Google Vertex AI.

• Implement model monitoring for performance degradation, data drift, model drift, latency, and reliability.

• Design cloud-based AI/ML architectures across AWS, Azure, and/or Google Cloud Platform.

• Optimize AI workloads for scalability, performance, availability, and cloud cost.

• Collaborate with data engineering teams to develop reliable data ingestion, transformation, and feature-engineering pipelines.

• Provide technical leadership, conduct code/design reviews, and mentor junior and senior engineers.

• Work with architects, product managers, and business stakeholders to define AI/ML roadmaps and technical solutions.

• Ensure AI solutions follow security, privacy, governance, explainability, and responsible-AI practices.

Required Technical Skills

Programming & Data

• Python – Expert

• SQL

• Pandas, NumPy

• PySpark

• REST APIs

• FastAPI / Flask

• Git and GitHub/GitLab

Machine Learning

• Supervised and Unsupervised Learning

• Regression and Classification

• Clustering

• Ensemble Learning

• Feature Engineering

• Model Selection and Optimization

• Hyperparameter Tuning

• Model Evaluation

• Time-Series Forecasting

• Recommendation Systems

• Anomaly Detection

Deep Learning & AI

• PyTorch

• TensorFlow

• Keras

• Neural Networks

• CNNs

• RNNs/LSTMs

• Transformers

• NLP

• Computer Vision

• Generative AI

• Large Language Models (LLMs)

Generative AI

• RAG (Retrieval-Augmented Generation)

• Prompt Engineering

• Embeddings

• Vector Databases

• Semantic Search

• LLM Fine-Tuning

• Model Evaluation

• AI Agents / Agentic AI

• LangChain / LlamaIndex

• OpenAI / Azure OpenAI or equivalent LLM platforms

• Guardrails and responsible AI practices

MLOps & ML Platforms

• MLflow

• Kubeflow

• Apache Airflow

• AWS SageMaker

• Azure Machine Learning

• Google Vertex AI

• Feature Stores

• Model Registry

• Model Monitoring

• Automated Retraining

• CI/CD for ML

Cloud & DevOps

• AWS / Azure / Google Cloud Platform

• Docker

• Kubernetes

• Terraform

• Jenkins / GitHub Actions / GitLab CI / Azure DevOps

• Infrastructure as Code

• Cloud monitoring and logging

Senior production-focused AI roles commonly combine ML frameworks with cloud, containerization, orchestration, and MLOps capabilities.

Data Engineering & Big Data

• Apache Spark / PySpark

• Databricks

• Kafka

• Data Lakes and Lakehouse Architecture

• ETL/ELT Pipelines

• Feature Engineering Pipelines

• AWS S3 / Azure Data Lake / Google Cloud Storage

• Snowflake or equivalent cloud data platforms

• SQL and NoSQL databases

Qualifications

• Bachelor''s or Master''s degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Engineering, or a related field.

• 11+ years of professional software/technology experience, with significant hands-on experience in AI/ML engineering.

• Strong experience taking ML models from POC/experimentation to production.

• Experience designing scalable enterprise AI/ML architectures.

• Strong understanding of software engineering principles, system design, APIs, testing, and production operations.

• Excellent problem-solving, communication, leadership, and stakeholder-management skills.