Bhartiya Converge
Website:
ayufest.com
Company:
https://www.linkedin.com/company/bhartiyaconverge
Seniority: Mid-Senior level
Industries: Business Consulting and Services
Job details:
Profile Overview
We are looking for an experienced AI Engineering Manager to lead the design, development, and delivery of enterprise-scale AI solutions that drive business transformation through Generative AI, AI Agents, Retrieval-Augmented Generation (RAG), and intelligent workflow automation.
This role combines technical leadership, hands-on AI engineering, and program delivery, requiring someone who can architect scalable AI platforms while leading cross-functional engineering teams to deliver production-ready solutions. The successful candidate will have deep expertise in modern AI frameworks, cloud-native engineering, MLOps, and software engineering best practices, with a proven track record of deploying secure, scalable, and governed AI solutions in enterprise environments.
Working closely with product, business, data, security, and technology teams, this individual will translate business challenges into AI-enabled products and services, establish engineering standards, mentor technical teams, and drive the adoption of responsible AI practices across the organization.
This is an opportunity to shape the organization's AI engineering capability by delivering next-generation AI platforms that improve productivity, automate decision-making, and enable intelligent business operations at scale.
AI Project Delivery & Execution
- Lead end-to-end delivery of AI-centric projects, with a primary focus on AI agents, Retrieval-Augmented Generation (RAG) systems, and automated decision workflows.
- Translate business use cases into scalable technical architectures, ensuring alignment with data, infrastructure, and product roadmaps.
- Manage full project lifecycle—from scoping and resource planning to execution, testing, deployment, and post-launch optimization.
- Define and track KPIs for AI system performance, accuracy, latency, and user adoption; ensure measurable impact.
Technical Leadership in AI Systems
- Architect and oversee implementation of RAG pipelines, including document ingestion, embedding models, vector databases (e.g., Pinecone, Milvus), retrieval logic, and LLM integration.
- Guide development of autonomous AI agents using frameworks like LangChain, LangGraph, Dify, or custom-built orchestration layers.
- Collaborate with data scientists and AI engineers to evaluate prompts, and implement guardrails for safety, fairness, and compliance.
- Ensure robust observability, logging, and monitoring of AI workflows to support debugging, auditing, and continuous improvement.
Engineering Excellence & Automation
- Champion MLOps practices: version control for datasets/models, reproducible pipelines, CI/CD for AI, and A/B testing of model outputs.
- Implement scalable backend services (APIs, event-driven microservices, MCP) to serve AI capabilities across enterprise applications.
- Leverage cloud-native technologies (AliCloud/AWS), containerization (Docker/Kubernetes), and Infrastructure-as-Code (Terraform) for reliable deployments
Cross-Functional Collaboration
- Serve as the primary liaison between AI engineering, product management, security, compliance, and business stakeholders.
- Present technical designs, risks, and progress updates clearly to both technical and non-technical audiences.
- Advocate for ethical AI principles, data privacy, and regulatory alignment throughout the development lifecycle.
Team Coordination & Mentorship
- Coordinate a hybrid team of internal engineers and vendor partners; assign tasks, monitor progress, and maintain accountability.
- Foster a culture of innovation, knowledge sharing, and agile delivery within the AI delivery pod.
- Mentor junior engineers in AI/ML best practices, prompt engineering, and responsible AI deployment.
Qualifications
- Bachelor’s or master’s degree in computer science, Artificial Intelligence, Software Engineering, or a related technical field.
- Minimum of 8+ years of hands-on experience delivering production-grade AI/ML projects, specifically involving AI agents, RAG systems, or intelligent workflow automation.
- Strong software engineering background with proven ability to lead technical teams and manage complex delivery timelines.
- Demonstrated experience with:
- Generative AI frameworks: LangChain, Dify, N8N, LangGraph, etc.
- Vector databases: Pinecone, Chroma, FAISS, Weaviate, or Milvus
- LLM APIs and platforms: OpenAI, Anthropic, Alibaba Qwen
- Prompt engineering, retrieval optimization, and evaluation metrics (e.g., BLEU, ROUGE, faithfulness, context relevance)
- Cloud platforms (AliCloud preferred, or AWS)
- Containerization and orchestration (Docker, Kubernetes)
- CI/CD tools (GitLab CI, GitHub Actions, Jenkins)
- Scripting languages: Python (required), JavaScript/TypeScript (preferred)
- Solid understanding of machine learning fundamentals, NLP, and transformer-based models.
- Familiarity with MLOps tools (MLflow, Kubeflow, Vertex AI) and observability stacks (Prometheus, Grafana, LangSmith).
- Excellent organizational, communication, and stakeholder management skills.
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