AI/ML Engineer

About the Role

We are looking for an inventive AI/ML Engineer who can create, train, and launch machine learning and artificial intelligence solutions that address practical business needs. This role calls for deep hands-on experience in machine learning methods, data handling, model productionization, and cloud environments.

Key Responsibilities

• Design, build, train, and tune machine learning and deep learning models.
• Create and support data pipelines that can scale for both training and inference workflows.
• Develop AI-enabled products using NLP, computer vision, generative AI, and predictive analytics.
• Use MLOps practices to move models into production systems.
• Work with large datasets, including cleaning, preprocessing, feature creation, and model assessment.
• Partner with data engineers, software developers, and business teams to clarify needs and deliver AI-based solutions.
• Track model quality and introduce ongoing enhancements.
• Investigate new AI tools, frameworks, and market developments.
• Build APIs and microservices to connect AI models with applications.
• Maintain standards for data protection, model governance, and compliance.

Required Qualifications

A bachelor’s or master’s degree in Computer Science, Artificial Intelligence, Data Science, Mathematics, Statistics, or a closely related discipline is required. Candidates should be strong Python programmers and have experience with machine learning libraries such as TensorFlow, PyTorch, Scikit-learn, and XGBoost. A solid grasp of supervised and unsupervised learning, deep learning, neural networks, natural language processing, computer vision, and preferably reinforcement learning is expected. The role also requires familiarity with SQL and NoSQL databases, as well as deployment tools such as Docker, Kubernetes, and MLflow. Experience with cloud platforms like AWS, Azure, or GCP is needed, along with working knowledge of Git.

Preferred Qualifications

Experience with generative AI and large language models is a plus. Practical exposure to LangChain, LlamaIndex, Hugging Face, OpenAI APIs, and vector databases such as Pinecone, Weaviate, ChromaDB, or FAISS will be valued. Prior work on RAG implementations, MLOps tools, CI/CD pipelines, Databricks, and Apache Spark is also preferred.

Technical Skills

Python, SQL, TensorFlow, PyTorch, Scikit-learn, Pandas, NumPy, Apache Spark, MLflow, Docker, Kubernetes, AWS/Azure/GCP, Git, REST APIs, and generative AI/LLM workflows.

Soft Skills

Strong analytical thinking, problem-solving ability, clear communication, effective collaboration, independent working style, teamwork, attention to detail, and a strong focus on quality.

Nice to Have

Experience with AI agent development, multi-agent systems, prompt engineering, LLM fine-tuning, knowledge graphs, MLOps certification, and cloud certifications from AWS, Azure, or GCP.