Senior AI/ML Engineer

Introduction:

We are seeking a highly skilled Senior AI/ML Engineer to design, develop, deploy, and optimize machine learning and artificial intelligence solutions. The ideal candidate will have strong experience in machine learning, deep learning, generative AI, MLOps, and cloud platforms, with the ability to translate complex business requirements into scalable AI solutions.

Responsibilities:

• Design and develop end-to-end machine learning and AI solutions for complex business problems.
• Build, train, evaluate, and optimize machine learning and deep learning models.
• Develop solutions using supervised, unsupervised, and reinforcement learning techniques.
• Work with Generative AI, Large Language Models (LLMs), NLP, RAG, embeddings, and prompt engineering.
• Design and implement production-ready ML pipelines and MLOps workflows.
• Deploy and manage AI/ML models across cloud and on-premises environments.
• Perform feature engineering, data preprocessing, model selection, and hyperparameter tuning.
• Develop scalable APIs and microservices for integrating ML models into enterprise applications.
• Monitor model performance, data quality, drift, latency, and reliability in production.
• Collaborate with data engineers, software engineers, data scientists, product managers, and business stakeholders.
• Establish best practices for model governance, versioning, reproducibility, security, and responsible AI.
• Conduct research and evaluate emerging AI/ML technologies and frameworks.
• Mentor junior engineers and provide technical leadership on AI/ML projects.

Requirements:

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related field.
• 12+ years of experience in software engineering, machine learning, or AI engineering.
• Strong programming skills in Python and experience with Java, Scala, or similar languages.
• Strong knowledge of Machine Learning, Deep Learning, NLP, and statistical modeling.
• Hands-on experience with PyTorch, TensorFlow, Scikit-learn, or equivalent frameworks.
• Experience with LLMs, Generative AI, RAG, vector databases, embeddings, and prompt engineering.
• Strong understanding of ML algorithms, model evaluation, optimization, and feature engineering.
• Experience building and deploying production-grade AI/ML applications.
• Experience with Docker, Kubernetes, CI/CD, Git, and MLOps practices.
• Experience with cloud platforms such as AWS, Azure, or Google Cloud.
• Strong knowledge of REST APIs, microservices, distributed systems, and scalable application architecture.
• Experience working with SQL and NoSQL databases and large-scale data processing.
• Strong analytical, problem-solving, communication, and leadership skills.

Preferred Qualifications:

• Experience with Azure OpenAI, Amazon Bedrock, Google Vertex AI, Hugging Face, LangChain, or LlamaIndex.
• Experience with vector databases such as Pinecone, FAISS, Milvus, Weaviate, or OpenSearch.
• Knowledge of model serving frameworks such as MLflow, Kubeflow, Ray Serve, or NVIDIA Triton.
• Experience with data engineering technologies such as Spark, Kafka, Databricks, or Snowflake.
• Experience implementing responsible AI, model security, explainability, and governance.
• Contributions to AI/ML research, open-source projects, or technical publications are a plus.

Technical Environment:

Languages: Python, SQL, Java/Scala
ML/AI: PyTorch, TensorFlow, Scikit-learn, Hugging Face, LLMs, NLP, Generative AI
GenAI: RAG, embeddings, vector databases, prompt engineering, fine-tuning
Cloud: AWS, Azure, Google Cloud
MLOps: MLflow, Kubeflow, Docker, Kubernetes, CI/CD
Data: Spark, Kafka, Databricks, Snowflake, SQL/NoSQL
Development: Git, REST APIs, microservices, Agile/Scrum

Key Competencies:

• AI/ML Architecture
• Machine Learning & Deep Learning
• Generative AI & LLM Engineering
• NLP and Computer Vision
• MLOps & Model Deployment
• Cloud AI Platforms
• Data Engineering
• Production Model Optimization
• Technical Leadership & Mentoring
• Problem Solving and Innovation