Engineering Manager – Fullstack & AI Engineering

 Posted 7 months ago
  
 India
  
10+ years experience
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AI Summary

Lead a high-performing engineering team to build intelligent, scalable systems leveraging AI and modern frameworks. Ensure delivery excellence and high engineering standards while mentoring and coaching team members.

Job Description: Engineering Manager – AI Engineering 

Location: [Remote – India] 

Experience: 8–14+ years 

Role Type: Full-time 

 

About Codvo.ai 

Codvo.ai is a next-gen technology consulting and product engineering company

focused on delivering AI-driven digital transformation. We blend engineering

excellence, innovation, and a strong problem-solving culture to craft solutions that

scale. We partner with global enterprises across FinTech, Digital Commerce,

HealthTech, and Emerging Tech ecosystems.

If you are passionate about building teams, solving deep technical challenges, and

applying AI to accelerate product development — this role is for you.


About the Role 

We are looking for an Engineering Manager – AI Engineering who combines people

leadership, technical depth, and hands-on programming expertise. You will lead a

high-performing engineering team that builds intelligent, scalable, and production

grade systems leveraging AI, automation, cloud, and modern full-stack frameworks.

This is a hybrid role that blends AI engineering, hands-on development, delivery

management, and team leadership.


Key Responsibilities 

 

AI Engineering Leadership 

• Lead teams building AI-powered applications, intelligent automation, and data

driven solutions.

• Guide engineers on integrating LLMs, vector search, prompt engineering, and AI

pipelines into products.

• Partner with AI/ML specialists to convert POCs into scalable production

systems.

 

Hands-On Technical Contribution 

(Must be 30–40% hands-on)

• Contribute actively to codebases using Node.js, Python, React.js, Angular, and

related frameworks.

• Design microservices, APIs, and cloud-native applications with AI components

embedded.

• Perform code reviews, provide architectural direction, and ensure high

engineering standards.

• Build reusable components, utilities, and internal tooling that accelerate AI

delivery.

 

Delivery & Execution 

• Own delivery excellence across sprints — scope planning, engineering

estimations, prioritization, dependency management, and stakeholder

alignment.

• Drive high-quality, on-time delivery for AI and application engineering initiatives.

• Ensure strong CI/CD, DevOps automation, and observability practices within the

team.

 

People Leadership 

• Lead, mentor, and coach a team of 6–12 engineers, AI developers, and tech

leads.

• Conduct 1:1s, performance reviews, skill assessments, and career coaching.

• Hire strong engineering talent and ensure smooth onboarding and competency

growth.

• Build a culture of ownership, innovation, continuous learning, and

experimentation.

 

Architecture & Technical Direction 

• Work closely with architects to define scalable AI + application architectures.

• Lead design discussions around distributed systems, API contracts, data flows,

and model integration.

• Evaluate new AI tools, frameworks, and cloud services; make recommendations

for adoption.

 

Cross-Functional Collaboration 

• Partner with Product Managers, UX designers, AI/ML engineers, and DevOps

teams to deliver cohesive solutions.

• Translate business problems into technical solutions with clear trade-offs and

measurable impact.

• Communicate progress, risks, and technical decisions clearly to stakeholders

and customers.


Qualifications 

 

Must Have 

• 8–14+ years of engineering experience with 2–5+ years in team/people

management.

• Strong hands-on programming expertise in:

o Node.js

o Python

o React.js

o Angular

• Experience delivering AI-enabled applications, generative AI features, or ML

integrated systems.

• Solid understanding of microservices, distributed systems, API design, and

cloud platforms (AWS/Azure/GCP).

• Proven ability to lead engineering teams and deliver complex projects end-to

end.

 

Good to Have 

• Experience with vector DBs (Pinecone, Qdrant, Weaviate, PGVector).

• Knowledge of LLM orchestration (LangChain, LlamaIndex, custom pipelines).

• Exposure to MLOps, model deployment, and AI observability.

• Experience with enterprise consulting/client-facing delivery.

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