Machine Learning Specialist

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.

About the role

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.

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.

Requirements

• 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.

Skills

• 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.