Senior Ai Developer

Capgemini

8 - 12 years

10 - 20 Lacs

hyderabad pune bengaluru

Posted:2 days ago| Platform:

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Skills Required

solution architecting generative ai machine learning

Work Mode

Hybrid

Job Type

Full Time

Job Description

Education & Experience

Bachelors degree in Computer Science, Data Science, Artificial Intelligence. A masters degree or PhD in AI, ML, or a related discipline is preferred.
5+ years of experience in solution architecture, with at least 3 years focused on designing and deploying generative AI, agentic AI, and ML solutions in enterprise environments.
Hands-on experience with AI/ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn, Hugging Face, LangChain) and agentic AI platforms for LOB applications.
Proven success in delivering AI/ML solutions for LOB use cases, such as automated customer service, content generation, predictive analytics.
Expertise in generative AI techniques (e.g., LLMs, GANs, VAEs), agentic AI systems (e.g., autonomous agents, reinforcement learning), and ML algorithms (e.g., regression, clustering, decision trees).
Proficiency in programming languages such as Python, R, or Java, with experience in AI/ML libraries and frameworks.
Strong knowledge of data engineering, including ETL pipelines, data lakes, and big data tools (e.g., Spark, Hadoop) for LOB data needs.
Experience with MLOps for deploying and managing generative AI, agentic AI, and ML models in production.
Familiarity with cloud platforms (Azure preferred) and enterprise systems integration (e.g., APIs, microservices).
Ability to align generative AI, agentic AI, and ML solutions with LOB objectives, such as increasing customer engagement, improving forecasting accuracy, or streamlining operations.
Strategic thinking to identify high-value LOB use cases for AI/ML adoption.
Proficiency in Agile and DevOps methodologies for rapid solution delivery.
Deep understanding of enterprise architecture and AI/ML integration patterns for LOB systems.
Commitment to ethical AI practices, particularly for generative AI outputs, agentic decision-making, and ML model fairness.

Responsibilities

Architect end-to-end AI and ML solutions, emphasizing generative AI (e.g., text, image, or code generation), agentic AI systems (e.g., autonomous agents for decision-making), and ML models (e.g., predictive analytics, classification) to address LOB use cases like personalized marketing, customer support automation, financial forecasting, and operational efficiency.
Provide technical expertise to development teams, ensuring generative AI, agentic AI, and ML solutions are scalable, secure, and integrated with enterprise systems.
Partner with LOB leaders, data scientists, and IT teams to identify high-impact use cases, define requirements, and translate business needs into robust AI/ML architectures.
Evaluate and recommend AI/ML frameworks and tools (e.g., TensorFlow, PyTorch, Scikit-learn, Hugging Face, LangChain, AutoGen).
Design data pipelines to support generative AI, agentic AI, and ML models, ensuring high-quality data for training, fine-tuning, and inference in LOB contexts, such as customer data for personalization or transactional data for predictive analytics.
Integrate AI/ML solutions with enterprise systems (e.g., Salesforce, SAP, or marketing platforms) to enable seamless LOB adoption and interoperability.
Optimize generative AI models, agentic systems, and ML algorithms for performance, cost-efficiency, and accuracy in production, addressing LOB-specific KPIs like prediction accuracy, response time, or customer satisfaction.
Establish governance frameworks for AI/ML solutions, ensuring compliance with regulations (e.g., GDPR, CCPA) and ethical standards, particularly for sensitive LOB applications like customer data handling or financial modeling.
Stay abreast of advancements in generative AI (e.g., multimodal models), agentic AI (e.g., multi-agent systems), and ML techniques (e.g., ensemble methods, reinforcement learning), proposing innovative solutions to enhance LOB outcomes, such as automated content creation, intelligent process orchestration, or predictive maintenance.

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