Principal Engineer

Job Summary:

One of our clients, a leading technology organization, is looking for an experienced Principal Engineer to provide technical leadership in designing, architecting, and delivering scalable, secure, and highly available AI-powered and cloud-native solutions.

The role requires a strong combination of software architecture, Generative AI/ML, cloud technologies, distributed systems, and hands-on engineering expertise. The candidate will work closely with engineering, product, and business stakeholders to define technical strategy, drive architecture decisions, mentor senior engineers, and build next-generation intelligent applications.

Key Responsibilities:

• Lead the end-to-end architecture, design, development, and delivery of scalable, secure, and highly available AI-powered solutions.
• Define and evolve technical architecture for LLM applications, RAG pipelines, agentic AI workflows, and AI/ML platforms.
• Design and implement distributed, cloud-native solutions using AWS, Azure, and/or GCP.
• Drive best practices around scalability, availability, fault tolerance, observability, security, and cost optimization.
• Champion AI-assisted software development using tools such as GitHub Copilot, Cursor, Claude Code, and other agentic coding tools.
• Evaluate and integrate open-source frameworks, libraries, and platforms to develop sustainable and vendor-agnostic solutions.
• Provide hands-on technical leadership through coding, proof-of-concepts, prototypes, code reviews, and critical architectural decisions.
• Establish engineering standards covering security, performance, scalability, observability, accessibility, privacy, and responsible AI.
• Lead architecture reviews and provide guidance on technical trade-offs, build-vs-buy decisions, and platform standardization.
• Define and guide DevOps, SRE, and MLOps practices including CI/CD, Infrastructure as Code, model deployment, monitoring, and continuous evaluation.
• Mentor and coach senior engineers, technical leads, and architects.
• Collaborate with engineering, product, and solutions teams to align technical strategy with business objectives and product roadmaps.
• Stay current with emerging technologies in Generative AI, LLMs, agentic systems, cloud computing, and applied ML.

Required Qualifications:

• Bachelor's or Master's degree in Computer Science, Engineering, or a related field, or equivalent professional experience.
• 12+ years of professional software engineering experience.
• Minimum 3+ years of experience in a Principal Engineer, Staff Engineer, or Enterprise Architect role.
• Proven experience architecting and delivering large-scale, production-grade AI/ML or data-intensive solutions.
• Strong experience designing and operating highly available, distributed enterprise systems.
• Hands-on experience with LLM applications, RAG systems, vector databases, prompt engineering, fine-tuning, or agentic workflows.

Technical Skills:

• Generative AI / LLM / AI/ML
• RAG and Vector Databases
• Agentic AI and AI Workflows
• Python, Java, Node.js, or Go
• AWS / Azure / GCP
• Cloud Architecture
• Microservices and Distributed Systems
• Event-Driven Architecture
• Kubernetes / Docker
• PyTorch / TensorFlow / scikit-learn
• Hugging Face
• LangChain / LlamaIndex
• OpenAI / Anthropic / AWS Bedrock APIs
• DevOps / SRE / MLOps
• CI/CD and Infrastructure as Code
• Terraform / CloudFormation
• Prometheus / Grafana / OpenTelemetry
• Secure Coding, IAM, Data Privacy, and Compliance

Leadership & Communication:

• Strong technical leadership and ability to influence across teams without direct reporting authority.
• Excellent written and verbal communication skills.
• Ability to explain complex technical concepts to technical and non-technical stakeholders.
• Strong mentoring and coaching capabilities.
• Experience working in agile, iterative, and customer-centric development environments.

Preferred Qualifications:

• Experience building and deploying agentic AI, multi-agent systems, or tool-using LLM applications in production.
• Experience with open-source projects or hybrid-source software portfolios.
• Experience with workflow orchestration tools such as Camunda, M8Flow, Airflow, or Temporal.
• Familiarity with government digital standards and compliance frameworks such as SOC 2, ISO 27001, GDPR, HIPAA, or FedRAMP.
• Knowledge of responsible AI, model governance, AI safety, bias evaluation, and LLM red-teaming.
• Cloud architecture or AI engineering certifications are an added advantage.