Senior AI/ML Engineer

Title: Senior AI/ML Engineer

Location: Hyderabad, TG (Onsite)

Duration: Full time (direct hire)

About client:

• Global AI & Innovation team to build next-generation AI platforms, autonomous agents, and real-time intelligence solutions that transform how a global enterprise operates and serves its customers.

Position Summary:

• Seeking a highly skilled Senior AI/ML Engineer to lead the design, development, deployment, and governance of enterprise-scale AI solutions.
• This role will be responsible for building scalable AI/ML platforms, Generative AI applications, Retrieval-Augmented Generation (RAG) solutions, agentic workflows, and real-time data pipelines that deliver measurable business value.
• The ideal candidate combines deep technical expertise with the ability to align AI initiatives to strategic business objectives.
• This role will report to the Global head of AI & Innovation and collaborate closely with the Data & Analytics, Enterprise architecture and Integrations, Information security, Legal & Data privacy teams.

Key Responsibilities:

AI Solution Engineering & Architecture:

• Design, develop, and deploy end-to-end AI/ML solutions, including microservices, RAG architecture, and agentic AI workflows.
• Build scalable and reusable AI services that support enterprise-wide business use cases.
• Evaluate and integrate emerging AI technologies, frameworks, and foundation models.

MLOps, Data Science & Advanced Analytics:

• Implement and maintain MLOps pipelines for model training, deployment, monitoring, versioning, and lifecycle management.
• Automate model deployment, performance monitoring, and drift detection processes.
• Optimize AI workloads for cost, scalability, and performance across Azure and AWS cloud platforms.
• Design, develop and deploy scalable, secured, predictive statistical ML models to continuously empower business functions. Real-Time Data Engineering
• Build low-latency data ingestion, processing, and orchestration pipelines supporting AI applications.
• Design and maintain vector databases, knowledge repositories, and semantic search capabilities.
• Enable real-time analytics and AI-driven decision-making using large-scale operational and transactional data. AI Governance, Security & Responsible AI
• Implement security-by-design principles across AI solutions and data platforms.
• Protect sensitive financial and operational data through robust access controls and governance frameworks.
• Mitigate risks related to prompt injection, hallucinations, data leakage, bias, and regulatory compliance.
• Ensure adherence to Responsible AI, privacy, and enterprise security policies. Business Partnership & Value Realization
• Collaborate with business leaders, product teams, and solution architects to identify high-value AI opportunities.
• Translate business problems into scalable AI solutions that drive operational efficiency, growth, and innovation.
• Measure and communicate business outcomes, model performance, and ROI from AI initiatives.

Required Qualifications:

• Bachelor’s or master’s degree in computer science, Data Science, Engineering, Artificial Intelligence, or related field.
• 7+ years of experience in AI/ML engineering, software engineering, data engineering, or cloud-based solution development.
• Hands-on experience with Generative AI, LLMs, RAG frameworks, AI agents, and vector databases.
• Strong foundation in statistics, predictive modeling, and machine learning.
• Experience selecting and implementing appropriate algorithms based on business objectives, data characteristics, and performance requirements.
• Strong proficiency in Python and modern AI/ML frameworks (PyTorch, TensorFlow, LangChain, Semantic Kernel, etc.).
• Experience with Azure AI Services, AWS AI/ML services, and cloud-native architectures.
• Knowledge of MLOps, CI/CD, containerization (Docker/Kubernetes), and modern software engineering practices.

Familiarity with supervised and unsupervised learning techniques, including:

• Predictive: Regression, Classification
• Tree-Based: Decision Trees, Random Forests, XGBoost, LightGBM
• Unsupervised: Clustering, Segmentation
• Forecasting & Recommendations
• Deep Learning & Neural Networks
• Reinforcement Learning
• Ability to interpret model results and translate insights into business recommendations.