Senior AI/ML Engineer

Job Title: Senior AI/ML EngineerLocation: Hyderabad, India – Hybrid Remote (3 days a week onsite)Company Overview:Circuitry.ai is at the forefront of artificial intelligence innovation, specializing in developing AI-driven software solutions that transform how industries operate. By harnessing the power of machine learning, predictive modelling, and generative AI, we help organizations unlock deep insights, drive automation, and make data-informed decisions at scale.Our AI solutions have been applied across a variety of domains, including automotive OEM, warranty analytics, manufacturing intelligence, and customer experience optimization. We are passionate about building AI systems that are not only powerful but also transparent, explainable, and aligned with real-world business outcomes.Role Overview:As a Senior AI/ML Engineer, you will be responsible for designing, developing, and deploying end-to-end machine learning solutions that are explainable, scalable, and impactful. You will work closely with AI Engineers, data engineers, and managers to translate business requirements into actionable AI/ML models and deploy them over cloud infrastructure.This role is ideal for someone who thrives on solving complex problems, enjoys exploring emerging technologies like GenAI frameworks, and can communicate insights effectively to both technical and business stakeholders.Key Responsibilities:Model Development & ExplainabilityDesign, develop, and deploy predictive and prescriptive ML models using state-of-the-art algorithms and tools.Implement explainable AI (XAI) frameworks to ensure transparency, interpretability, and trust in model predictions.Evaluate model fairness, bias, drift, and performance over time; recommend retraining or improvements.End-to-End ML EngineeringOwn the full lifecycle: data exploration, feature engineering, model training, validation, deployment, and monitoring.Operationalize models using CI/CD pipelines on cloud platforms (AWS, GCP, or Azure).Collaborate with data engineers to design scalable data pipelines for model input/output.GenAI & Emerging Tech IntegrationStay up to date with the latest advancements in Generative AI, LLMs, vector databases, and embedding-based retrieval systems.Experiment with integrating GenAI capabilities (e.g., summarization, reasoning, anomaly explanation) into predictive workflows.Evaluate new frameworks (LangChain, LlamaIndex, Hugging Face, OpenAI APIs) for business relevance.Data Analysis & ResearchConduct in-depth exploratory data analysis to uncover trends, anomalies, and actionable insights.Document experimental results, methodologies, and findings comprehensively for cross-functional consumption.Contribute to internal knowledge sharing and best practices in model governance and reproducibility.Collaboration & MentorshipMentor junior data scientists and engineers, providing technical guidance on best practices in ML development and deployment.Work closely with Product Managers, TPMs, and business stakeholders to align model outputs with business objectives.Clearly communicate technical results, model limitations, and recommendations to non-technical audiences.Qualifications:Required Skills:Education: Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, or related field.Experience: Minimum 4+ years of exclusive experience building and deploying ML/AI models in production environments.Programming: Expert in Python; proficient with key ML/data libraries (pandas, scikit-learn, TensorFlow/PyTorch, SHAP/LIME).Explainability: Strong understanding of model interpretability techniques, fairness, and trust frameworks.Cloud & Deployment: Hands-on with ML model deployment and MLOps using AWS Sagemaker, GCP Vertex AI, or Azure ML.Version Control & CI/CD: Familiarity with Git, Docker, and CI/CD tools for model lifecycle management.Communication: Excellent documentation, analytical reasoning, and stakeholder management skills.Preferred / Nice to Have:Experience working in automotive OEM, warranty, or manufacturing domains.Exposure to Generative AI tools and frameworks (OpenAI APIs, Hugging Face Transformers, LangChain, etc.).Knowledge of time series forecasting, anomaly detection, or failure prediction models.Experience integrating models with cloud-based applications via REST APIs or microservices.