BTS C: Engineering & Agentic AI Consultant

Pune April 6, 2026 Full Time

Job Description: Engineering & Agentic AI Consultant

We are seeking a hands-on, results-driven Engineering & Agentic AI Consultant with deep expertise in AWS services, containerization (Docker & Kubernetes), microservices architecture, system design, and emerging Agentic AI systems. In this role, you will lead teams in designing, building, and deploying cloud-native and AI-powered applications. You will work closely with clients to architect and implement intelligent, autonomous AI solutions leveraging LLMs, multi-agent systems, and modern AI orchestration frameworks. You will also ensure technical excellence, manage engineering best practices, and mentor engineers in both cloud and AI domains.

Key Responsibilities:

Technical Leadership & Team Management:

Lead cross-functional teams of engineers and AI practitioners, ensuring high-quality code, effective collaboration, and adherence to best practices across cloud and AI systems. Foster a culture of innovation, experimentation, and continuous improvement.

Cloud & AI Architecture Design:

Design and architect scalable, resilient, and highly available cloud solutions on AWS using services such as EKS, Lambda, RDS, S3, and CloudFormation. Architect and implement Agentic AI systems, including LLM-based workflows, multi-agent frameworks, tool integration, and retrieval-augmented generation (RAG) pipelines.

Agentic AI Solution Development:

Develop and deploy AI agents capable of autonomous reasoning, planning, and tool usage. Implement prompt engineering, context management, memory strategies, guardrails, and evaluation frameworks to ensure reliability, safety, and performance.

Client Engagement & AI Consulting:

Act as a technical expert in client engagements, advising on cloud modernization and AI adoption strategies. Assess client use cases for AI enablement, define AI architecture roadmaps, and deliver production-ready solutions.

Mentorship & Capability Building:

Mentor engineers on cloud-native design and AI engineering best practices, including LLMOps, evaluation frameworks, monitoring, observability, and responsible AI practices.

Reporting & Dashboards (Good to Have):

Build reporting dashboards leveraging AWS services like QuickSight or integrate third-party tools to track AI system performance, usage analytics, and business impact metrics.

Required Qualifications:

Experience:

6+ years of hands-on software engineering experience, with at least 2 years in a technical leadership role. Proven experience delivering complex cloud-native or AI-driven applications for enterprise clients.

AWS & Cloud Expertise:

Strong experience with AWS services such as EKS, S3, Lambda, RDS, VPC, and CloudFormation. Experience designing scalable and secure architectures on AWS.

Agentic AI & LLM Expertise:

Hands-on experience with Large Language Models (LLMs) such as OpenAI, Anthropic, or open-source models. Experience building AI agents, multi-agent systems, RAG pipelines, embeddings-based search, and vector databases (e.g., Pinecone, FAISS, OpenSearch).

Containerization & DevOps:

Extensive experience with Docker and Kubernetes for containerized deployments. Experience with CI/CD tools such as Jenkins, GitLab CI, or AWS CodePipeline.

Microservices & Distributed Systems:

Experience designing microservices architectures and distributed systems with strong understanding of fault tolerance, observability, and performance optimization.

Client-Facing Consulting Experience:

Experience working directly with clients to define technical and AI solutions, align on goals, and manage stakeholder expectations.

Preferred Skills:

Experience with AI orchestration frameworks such as LangChain, Semantic Kernel, or similar.
Experience with LLMOps, model evaluation frameworks, monitoring, and guardrail implementation.
Experience with serverless architectures and event-driven AI workflows.
Strong understanding of Responsible AI, governance, and data privacy best practices.
Strong communication skills with the ability to explain complex AI and cloud concepts to non-technical stakeholders.