Job Description (JD) – Machine Learning Lead
Company: Circuitry.ai
Location: Hyderabad (Onsite)
Role: Machine Learning Lead
Experience: 6–12+ 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
- 6–12+ 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
- Successful deployment of scalable AI and ML solutions.
- Improvement in model performance and business KPIs.
- Reduced model deployment cycle through MLOps automation.
- Team development, retention, and technical excellence.
- Delivery of innovative GenAI capabilities that create measurable customer value.
Thanks
Satya Puruma
[Confidential Information]
