AI Software Engineer

Entrust is looking for a highly motivated AI Software Engineer to join the AI CoE Team, led by the Director of AI and Automation. In this role, you will help accelerate innovation across the organization by designing and delivering AI-powered solutions that improve operational efficiency, streamline daily workflows, and strengthen business outcomes. You will also contribute to the definition, prototyping, and validation of next-generation digital security solutions for agentic AI systems, helping shape new products and capabilities in a rapidly evolving field. The ideal candidate will be comfortable moving ideas from pilot to MVP to production, with a strong focus on applying AI/ML technologies to real-world challenges.

Key Responsibilities

- Analyze and Innovate: Partner with cross-functional teams to identify opportunities, define requirements, and deliver high-impact innovation projects across Entrust's security product segments.
- AI Development: Design, build, and implement modern AI solutions that apply the right technologies to meet project and business needs.
- Collaboration: Communicate ideas clearly, challenge assumptions constructively, and build alignment across teams to drive shared success.
- RESTful APIs: Develop and optimize REST APIs that enable reliable, productive communication between application components, services, and AI-enabled workflows.
- Scalable Solutions: Design scalable, maintainable solutions using continuous integration and delivery practices.
- Documentation: Create clear, concise documentation that supports maintainability, knowledge sharing, and system adoption.
- Testing: Establish and maintain robust testing practices to ensure reliability, performance, and production readiness.
- Cloud Computing: Use cloud platforms to deploy secure, scalable, and cost-effective AI and software solutions.
- LLM and GenAI Application Engineering: Design and implement LLM-powered applications using prompt engineering, retrieval-augmented generation, embeddings, vector search, tool/function calling, and model evaluation techniques.




- AI Lifecycle and MLOps: Support model deployment, versioning, monitoring, observability, evaluation, and continuous improvement of AI systems across pilot, MVP, and production stages.
- Responsible AI and Governance: Embed privacy, security, explainability, human oversight, bias mitigation, ethical AI practices, and compliance requirements into AI solution design and delivery.
- Data Engineering for AI: Work with structured and unstructured data sources, data pipelines, data quality checks, feature preparation, knowledge repositories, and enterprise knowledge retrieval patterns.
- Production Reliability and Security: Troubleshoot production issues, tune performance, implement secure API design, apply security-by-design principles, and support logging, monitoring, incident response, and operational reliability.

Required Skills

The following skills are required. Candidates should be able to demonstrate practical experience and proficiency in these areas.

- Critical Thinking: Strong analytical and problem-solving skills, with the ability to evaluate complex challenges, compare solution options, and make sound technical decisions.
- AI Agent Development: 1+ year of practical experience
- Hands-on familiarity with Generative AI technologies and experience building AI agents programmatically or with AI coding tools such as Codex, GitHub Copilot, or Claude Code.
- Understanding of agent architectures, tool orchestration, memory, planning, evaluation, and emerging interoperability protocols such as MCP and A2A.
- Experience with LLM application patterns including RAG, embeddings, vector databases, semantic search, prompt engineering, tool/function calling, prompt regression testing,



and LLM evaluation frameworks.
- Familiarity with AI development frameworks and platforms such as LangChain, AutoGen, Anthropic SDK, Microsoft Foundry, or comparable low-code tools such as Copilot Studio.
- Familiarity with cloud AI platforms and services such as Azure OpenAI, AWS Bedrock, Amazon SageMaker, Google Vertex AI, Microsoft Foundry, or equivalent enterprise AI platforms.
- Working knowledge of MLOps and AI observability tools such as MLflow, Kubeflow, model registries, CI/CD for ML, monitoring dashboards, and model performance tracking.
- Experience with vector and search technologies such as FAISS, Pinecone, Weaviate, Azure AI Search, OpenSearch, Elasticsearch, or similar retrieval platforms.
- Understanding of secure AI engineering practices including secure coding, API security, authentication and authorization, IAM, secrets management, data protection, threat modeling, and safe handling of sensitive information.
- Ability to test AI systems using hallucination checks, safety testing, adversarial testing, red-teaming, model output validation, evaluation datasets, and quality gates for production readiness.
- Experience working in Agile product delivery environments, including rapid prototyping, experimentation, backlog refinement, user story definition, stakeholder feedback loops, and productization of AI capabilities.

- Proficiency with core engineering tools and environments, including:

- Git for version control and collaborative software development.
- Docker for containerization and reproducible development environments.
- Linux operating systems and scripting.

- Programming Skills: 8+ years of experience developing software in three or more object-oriented languages, preferably Go and/or Python, with strong knowledge of design patterns and UML.

- Web API Development: Experience designing and implementing REST APIs and service integrations.
- Database Design: Experience working with SQL databases such as PostgreSQL and NoSQL databases such as MongoDB.

📌 AI Software Engineer (Bengaluru)
🏢 TRIGENT SOFTWARE PRIVATE
📍 Bengaluru