Machine Learning Specialist

Job Description Job Description (JD) Machine Learning Lead Company: Circuitry.ai Location: Hyderabad (Onsite) Role: Machine Learning Lead Experience: 612 Years Employment Type: Full-Time About Circuitry.ai Circuitry.ai is focused on building next-generation AI-powered products and intelligent automation solutions that help organizations unlock business value through machine learning, generative AI, and data-driven decision-making. We are looking for a passionate and hands-on Machine Learning Lead to drive the design, development, and deployment of scalable AI solutions. Position Summary The Machine Learning Lead will be responsible for leading the end-to-end development of machine learning and generative AI solutions, managing a team of ML engineers and data scientists, and delivering production-grade AI systems. The ideal candidate combines strong technical expertise with leadership capabilities and a strategic mindset to transform business challenges into impactful AI products. Key Responsibilities Leadership & Strategy Lead and mentor a team of Machine Learning Engineers and Data Scientists. Define and execute the ML and AI roadmap aligned with business objectives. Establish best practices for model development, deployment, monitoring, and governance. Collaborate with Product, Engineering, Data, and Business stakeholders to identify AI opportunities. Drive innovation in AI, Machine Learning, Deep Learning, and Generative AI technologies. Machine Learning Development Design, build, train, validate, and deploy machine learning models for real-world business applications. Develop predictive analytics, recommendation systems, NLP, computer vision, and anomaly detection solutions. Evaluate and implement state-of-the-art algorithms and frameworks. Optimize model performance, scalability, reliability, and cost-efficiency. Generative AI & LLMs Develop and deploy solutions leveraging Large Language Models (LLMs). Build Retrieval-Augmented Generation (RAG) pipelines and AI agents. Fine-tune, evaluate, and optimize foundation models. Implement prompt engineering, model orchestration, and guardrails for enterprise AI applications. MLOps & Deployment Establish MLOps pipelines for continuous integration and deployment of ML models. Build model monitoring systems to track performance, drift, and reliability. Work with cloud platforms to deploy scalable AI solutions. Ensure security, compliance, and governance standards are maintained. Stakeholder Management Translate complex technical concepts into business outcomes for leadership teams. Provide technical guidance during customer discussions and solution design workshops. Partner with cross-functional teams to deliver AI solutions on time and within scope. Required Qualifications Education Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related field. Experience 612 years of experience in Machine Learning, AI, Data Science, or related domains. 3 years of experience leading ML/AI teams. Proven track record of deploying machine learning solutions into production environments. Technical Skills Strong expertise in Python and ML frameworks such as TensorFlow, PyTorch, Scikit-learn, and Hugging Face. Experience with LLMs, Generative AI, RAG architectures, AI agents, and vector databases. Strong knowledge of NLP, Deep Learning, and statistical modeling. Experience with MLOps tools such as MLflow, Kubeflow, Airflow, or similar. Hands-on experience with Docker, Kubernetes, and microservices architectures. Experience working with cloud platforms such as Azure, AWS, or GCP. Proficiency in SQL and data engineering concepts. Preferred Qualifications Experience building enterprise-grade AI products. Familiarity with LangChain, LlamaIndex, Semantic Kernel, AutoGen, CrewAI, or similar frameworks. Experience with AI governance, responsible AI, and model risk management. Contributions to open-source AI/ML projects or published research papers. AI/Cloud certifications from Azure, AWS, or GCP. Key Competencies Strategic thinking and problem-solving. Strong leadership and team management skills. Excellent communication and stakeholder engagement. Ability to balance innovation with business outcomes. Strong ownership and execution mindset. Success Metrics Suc .