Machine Learning Engineer

We are looking for a highly skilled Machine Learning Engineer to design, build, deploy, and optimize machine learning and AI-powered solutions that drive business impact. The ideal candidate will have extensive experience developing production-grade machine learning systems, working with modern AI tools and frameworks, and collaborating with cross- functional teams to deliver scalable AI products. This role is ideal for someone passionate about machine learning, Generative AI, Large Language Models (LLMs), MLOps, and building intelligent applications that solve real-world business challenges. Key Responsibilities • Design, develop, train, test, and deploy machine learning models in production environments. • Build scalable AI and machine learning pipelines for data processing, model training, evaluation, and deployment. • Develop and optimize predictive models, recommendation systems, NLP solutions, computer vision applications, and AI-driven products. • Collaborate with Product Managers, Data Scientists, Software Engineers, and business stakeholders to translate business requirements into AI solutions. • Implement and maintain MLOps best practices, including model monitoring, versioning, retraining, and performance optimization. • Evaluate and integrate emerging AI technologies, tools, and frameworks into existing systems. • Build and deploy Generative AI applications leveraging Large Language Models (LLMs) and AI Agents. • Conduct model experimentation, A/B testing, and performance analysis. • Optimize model inference, scalability, and cost efficiency across cloud environments. • Ensure data quality, security, and compliance standards are maintained throughout the ML lifecycle. • Stay current with advancements in Machine Learning, Deep Learning, Generative AI, and AI infrastructure. Required Qualifications • Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, Mathematics, or a related field. • 5+ years of hands-on experience developing and deploying Machine Learning solutions. • Strong proficiency in Python and machine learning libraries such as Scikit-learn, TensorFlow, PyTorch, XGBoost,and Pandas. • Experience building end-to-end ML pipelines and deploying models into production. • Strong understanding of supervised and unsupervised learning, deep learning, feature engineering, and model evaluation techniques. • Experience working with SQL and large-scale datasets. • Hands-on experience with cloud platforms such as AWS, Azure, or Google Cloud Platform. • Strong software engineering fundamentals, including Git, CI/CD, testing, and API development. • Excellent problem-solving and communication skills. Preferred Qualifications • Experience with Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI Agents, and prompt engineering. • Hands-on experience with AI frameworks and tools such as OpenAI,Anthropic Claude, Gemini, LangChain, LlamaIndex, CrewAI, AutoGen, or similar technologies. • Experience building NLP, conversational AI, chatbots, recommendation engines, or computer vision applications. • Familiarity with vector databases such as Pinecone, Weaviate, Chroma, or FAISS. • Experience with distributed computing frameworks such as Spark or Ray. • Experience working in startup or high-growth environments. Technical Skills Machine Learnings AI • Machine Learning • Deep Learning • Natural Language Processing (NLP) • Computer Vision • Generative AI • Large Language Models (LLMs) • Reinforcement Learning (Preferred) Programming Frameworks • Python • Scikit-learn • TensorFlow • PyTorch • XGBoost • Pandas • NumPy AI Tools Platforms • OpenAI API • Claude API • Gemini API • LangChain • LlamaIndex • CrewAI • AutoGen • Hugging Face MLOps s Infrastructure • MLflow • Kubeflow • Airflow • Docker • Kubernetes • CI/CD Pipelines Cloud Platforms • AWS SageMaker • Google Vertex AI • Azure Machine Learning