Principal Engineer (AI) for Database Technology
$31,200–$31,200 year
On-siteBengaluru, Karnataka, India
Bengaluru, Karnataka, IndiaOn-siteFull Time$31,200–$31,200 yearSenior LevelEnterprise
Full TimeSenior LevelEnterprise
Job Summary
Advise leadership on developing applications, network, information security, database, operating systems, or web technologies for complex business needs across multiple groups. Lead strategy and resolution of highly complex challenges requiring in-depth evaluation across multiple enterprise areas, delivering long-term, large-scale solutions with vision and innovation. Translate advanced technology experience and organizational strategic objectives into technical engineering solutions while providing direction on implementing significant business solutions. Strategically engage with all levels of professionals and managers to serve as an expert advisor, maintaining knowledge of industry best practices and recommending innovations that enhance operations or provide competitive advantage.
Required Qualifications
- 7+ years of Engineering experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education
- Advanced degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Engineering, Mathematics, or a related technical discipline
Desired Qualifications
- 7+ years of overall technology experience in software engineering, enterprise architecture, platform engineering, database technology, machine learning, or AI-led transformation roles
- 7+ years of experience in architecture and technical leadership, including defining technology strategy, target-state architecture, roadmaps, standards, and reusable engineering patterns for large enterprise platforms
- 3+ years of hands-on experience in AI/ML, Generative AI, or intelligent automation, including architecting and delivering production-grade AI solutions that create measurable business value with integrations to enterprise approved AI tooling
- Strong expertise in Generative AI, Agentic AI, Machine Learning, LLM-based applications, RAG architectures, AI agents, knowledge systems, prompt engineering, model evaluation, and intelligent workflow automation
- Proven experience designing and delivering enterprise-scale AI platforms and services, including reusable APIs, orchestration frameworks, developer toolkits, reference architectures, and common capabilities that accelerate adoption across teams
- Deep understanding of cloud-native architecture, distributed systems, data platforms, API design, microservices, observability, scalability, reliability, and production deployment practices
- Demonstrated experience partnering with executive leadership, business stakeholders, CIO organizations, architects, engineering teams, cybersecurity, risk, compliance, and governance partners to shape AI strategy and drive enterprise transformation
- Strong knowledge of AI governance, responsible AI, model risk management, privacy, security, regulatory compliance, ethical AI principles, data protection, and operational risk controls
- Experience evaluating and adopting modern AI technologies, frameworks, platforms, and vendors, including supporting build-vs-buy decisions, technology rationalization, cost optimization, scalability assessment, and total cost of ownership analysis
- Ability to lead large, complex, cross-functional initiatives involving cloud platforms, data engineering, cybersecurity, enterprise applications, AI services, and automation platforms
- Demonstrated ability to serve as a senior technical authority, providing architecture governance, design reviews, engineering guidance, mentoring, and thought leadership to senior engineers and architects
- Excellent communication, influencing, and storytelling skills with the ability to translate complex AI concepts into clear business outcomes, architecture decisions, risks, and investment recommendations
- Good understanding of database platforms like Oracle, PostgreSQL, MongoDB
- Experience building and deploying solutions using modern AI/ML and GenAI frameworks such as LangChain, LangGraph, Semantic Kernel, LlamaIndex, AutoGen, CrewAI, Hugging Face, MLflow, TensorFlow, PyTorch, or similar technologies
- Hands-on experience with cloud AI services and platforms such as Devin AI, Azure OpenAI, AWS Bedrock, Google Vertex AI, Databricks, Snowflake, Kubernetes, OpenShift, or other enterprise cloud platforms
- Experience implementing agent orchestration patterns, multi-agent systems, workflow automation, tool/function calling, memory management, contextual retrieval, and human-in-the-loop controls
- Strong understanding of LLMOps/MLOps practices, including model lifecycle management, prompt/version management, evaluation pipelines, guardrails, monitoring, drift detection, observability, and automated deployment
- Experience designing AI solutions for highly regulated environments, preferably in financial services, banking, healthcare, insurance, or other risk-sensitive industries
- Working knowledge of cybersecurity architecture, identity and access management, secrets management, data classification, encryption, secure SDLC, vulnerability management, and threat modeling for AI platforms
- Experience with enterprise knowledge management, semantic search, vector databases, embeddings, metadata enrichment, data lineage, and retrieval optimization
- Proven ability to establish engineering standards, architecture guardrails, reusable design patterns, coding best practices, and governance models for AI adoption at enterprise scale
- Experience leading innovation initiatives, proof of concepts, technology incubations, hackathons, or AI transformation programs that move from experimentation to production adoption
- Strong vendor management experience, including evaluating AI platforms, third-party tools, licensing models, commercial terms, platform fit, operational readiness, and long-term maintainability
- Recognized thought leadership through whitepapers, internal architecture forums, patents, publications, conference talks, technical communities, or enterprise-wide engineering leadership
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