
Client: Capgemini
Location: Hyderabad
Experience: 8+ Years
Senior Software Engineer
As a Senior Software Engineer, you will design, develop, test, and deploy sophisticated Generative AI applications. You will work within a dedicated engineering pod, partnering with technical leaders to translate architectural designs into robust, scalable, and secure software. This role requires hands-on development experience and a strong interest in solving complex technical problems using modern AI capabilities.
In this role, you will:
- Lead moderately complex initiatives and deliverables within technical engineering environments.
- Contribute to large-scale planning of strategies across Consumer Technology.
- Design, code, test, debug, and document applications and services, including upgrades and deployments.
- Review technical challenges that require in-depth evaluation of technologies, procedures, and engineering approaches.
- Resolve moderately complex issues while guiding teams to meet existing and emerging business needs.
- Collaborate with peers, colleagues, and mid-level managers to resolve technical challenges and meet project goals.
- Lead projects and act as an escalation point, providing direction to less experienced engineers.
- Design, develop, and deploy AI applications using enterprise APIs, LLMs, agent frameworks, and related technologies.
- Implement prompt engineering, retrieval-augmented generation, fine-tuning, and agentic design patterns.
- Integrate LLM models with existing enterprise systems and ensure that AI solutions meet governance, security, and compliance standards.
- Troubleshoot complex application and model-related issues and contribute to the continuous improvement of AI systems.
- Assist and mentor engineers in advanced software development and AI engineering practices.
- Stay informed of advancements in AI, LLMs, and agent frameworks and apply relevant updates to products and systems.
Required Qualifications:
- 4+ years of software engineering experience or equivalent through a combination of work experience, training, military service, or education.
- 2+ years of experience working with Generative AI, large language models, or foundation models.
- 2+ years of experience with either GCP, Azure, Kubernetes, or OpenShift.
- 2+ years of experience with Python.
- 2+ years of experience with REST API development and containerization technologies such as Docker and Kubernetes.
- 2+ years of experience using Git for source code version control, including branching, pull requests, and collaborative development workflows.
Desired Qualifications:
- Understanding of cloud security principles including identity and access management, encryption, and network security in public or hybrid cloud environments.
- Experience working in highly regulated industries such as financial services.
- Experience as a technical lead or architect, including mentoring senior engineers.
- Experience integrating or contributing to open-source AI or ML projects.
- Experience integrating applications with enterprise data platforms, APIs, and secure data pipelines.
- Strong communication and documentation skills to collaborate across engineering, product, and oversight teams.
Preferred Skills and Experience:
- Experience with LLM development using OpenAI, Anthropic, or Google Gemini models.
- Experience with agentic frameworks and AI workflow orchestration.
- Experience designing applications that incorporate retrieval-augmented generation, fine-tuning, and structured prompting.
- Experience with vector databases and retrieval systems such as Elasticsearch, OpenSearch, Pinecone, or Weaviate.
- Experience with LLM evaluation, observability, and monitoring, including latency, cost, accuracy, grounding, drift detection, and safety assessments.
- Familiarity with ML lifecycle tools and processes such as feature stores, model registries, and CI/CD pipelines for AI services.
- Familiarity with responsible AI principles, compliance, and governance processes related to AI systems in regulated environments.
- Experience optimizing AI application performance, including prompt efficiency, model selection, caching, batching, and cost management.

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