Artificial Intelligence Engineer

Job Description

Role: AI/ML Engineer

Visa: H1B, GC, H4EAD

Experience: 8+ Years

Employment Type: Contract

Job Summary

We are seeking an AI/ML Engineer with a broad focus on both advanced artificial intelligence techniques (e.g., deep learning, NLP, computer vision) and classical machine learning. The ideal candidate will design, implement, and deploy AI systems that solve complex business challenges at scale. This role involves end-to-end ownership of AI solutions—from data preprocessing and model training to production deployment and monitoring.

Responsibilities

Develop and optimize AI-driven solutions using various techniques (deep learning, classical ML, reinforcement learning, etc.).

Design data pipelines (ETL/ELT) for collecting, cleaning, and transforming raw data to feed into ML/AI models.

Implement advanced architectures (CNNs, RNNs, Transformers) for projects like NLP, computer vision, or recommendation systems.

Leverage MLOps frameworks (MLflow, Kubeflow) to automate model deployment, versioning, and monitoring.

Integrate AI models into microservices or REST/GraphQL APIs, ensuring efficient inference at scale.

Collaborate with cross-functional teams (data engineering, devops, product) to gather requirements and align on deliverables.

Utilize big data tools (Spark, Hadoop) and cloud platforms (AWS, Azure, GCP) for large-scale AI workloads.

Monitor model performance in production—identify data drift, maintain or retrain models, and ensure high availability.

Deploy and orchestrate containerized solutions using Docker, Kubernetes, and Terraform.

Stay updated on emerging AI trends and techniques (GANs, diffusion models, LLMs) and apply them where relevant.

Required Skills & Qualifications

Experience: 7+ years in ML or AI engineering roles.

Programming: Proficient in Python (must). Familiarity with Java or C++ is a bonus.

AI/ML Frameworks: Expert in TensorFlow or PyTorch, plus Scikit-learn for classical ML.

Data Handling: Experience with SQL/NoSQL databases (PostgreSQL, MongoDB) and big data frameworks (Spark, Hadoop).

MLOps & DevOps: Hands-on with Docker, Kubernetes, and CI/CD pipelines (Jenkins, GitLab CI, GitHub Actions).

Cloud: AWS, Azure, or GCP for AI/ML deployments at scale.

AI Concepts: Familiarity with NLP, CV, deep reinforcement learning, or large language models.

Software Engineering: Git for version control, agile methods, testing & debugging.

Soft Skills: Strong communication, collaboration, and a problem-solving mindset.