AI/ML- Engineer/Lead/Associate Architect

Covasant Technologies; Hyderabad; On-site; INR 2,500,000 - 4,000,000; Full time

Benefits offered by this job

Certification support

Technical mentoring

Exposure to global enterprise clients

Job summary

Covasant Technologies in Hyderabad is seeking an experienced AI/ML Lead/Architect to anchor the delivery of enterprise AI projects. You will design, build, deploy, and optimize production-grade AI/ML systems, ensuring scalability, security, and regulatory alignment.

You will mentor engineers, interface with clients, and guide cross-functional teams across data pipelines, LLMs, MLOps, and cloud infrastructure, leveraging Python, PyTorch, LangChain, and Kubernetes to deliver high-impact solutions.

Qualifications

• B.Tech/M.Tech in Computer Science, Data Science, or related field.
• 5+ years in software architecture or engineering with 5+ years in applied AI/ML delivery.
• Experience in productionizing AI/ML models and building enterprise AI apps.

Responsibilities

• Own end-to-end technical architecture and solution integrity during project delivery.
• Translate blueprints into detailed designs, backlog, and integration plans.
• Lead design reviews aligned with AI platform, MLOps, and Agentic AI best practices.
• Review code, infrastructure scripts, and ML pipelines for quality and reliability.
• Oversee data pipelines, model training, LLMOps, and deployment workflows.
• Collaborate with cross-functional teams and act as technical anchor for clients.

Skills

Python

ML Frameworks

LLMs

MLOps

Data Pipelines

GCP/Azure

Kubernetes

Terraform

LangChain

Architectural design

Education

B.Tech/M.Tech in CS/DS

Tools

MLflow

Airflow

KServe

Kubeflow

Spark

Weaviate

Qdrant

LangChain

Kubernetes

Terraform

Job description

Role: AI/ML- Engineer/Lead/Architect

Experience: 6+ years

Location: Hyderabad(On-site_5-Days)

About the Role

We are looking for experienced AI/ML- Lead/Architects to join our AI Engineering service line. In this role, you will anchor the technical delivery of enterprise AI/Agentic AI projects post deal-closure. You will take over from solution architects and lead the design, build, deployment, and optimization of AI/ML systems ensuring production-grade quality, scalability, and compliance. You will interface with cross-functional teams, manage engineering complexity, and ensure value realization for customers across industries such as BFSI, HLS, Manufacturing, CMT, Retail, and Energy.

Key Responsibilities

Architecture & Technical Leadership

• Own the end-to-end technical architecture and solution integrity during project delivery.
• Translate solution blueprints into detailed technical designs, backlog, and integration plans.
• Lead detailed design reviews, ensure alignment with AI platform, MLOps, and Agentic AI best practices.
• Select appropriate frameworks, APIs, libraries, and cloud-native services for implementation.

Project Execution & Delivery Oversight

• Serve as technical anchor for customer AI/ML and Agentic AI projects.
• Guide engineering teams on modular, secure, and reusable implementation strategies.
• Review and validate code, infrastructure scripts, and ML pipelines for quality, performance, and reliability.
• Oversee data pipelines, model training, LLMOps, and model deployment workflows.

Hands-on Engineering & Problem Solving

• Provide hands-on support for complex components: LLM pipelines, vector stores, finetuning, evaluations.
• Resolve system integration challenges across APIs, knowledge stores, and orchestration layers.
• Implement or validate MLOps workflows using MLflow, Airflow, Argo, KServe, BentoML, etc.

Quality, Compliance & Observability

• Embed observability, safety, and compliance into the AI/ML lifecycle.
• Integrate with AI governance platforms for model tracking, versioning, audits, and risk controls.
• Ensure alignment with enterprise and regulatory standards like NIST AI RMF, EU AI Act, and internal responsible AI policies.

Collaboration & Customer Engagement

• Act as the technical point of contact for client-side engineering and data science teams during delivery.
• Participate in sprint planning, status reviews, and change control boards.
• Support knowledge transfer, UAT, documentation, and post-deployment handoffs.

Required Qualifications

Education

• B.Tech/M.Tech or equivalent in Computer Science, Data Science, or a related field.

Experience

• 5+ years in software architecture or engineering with 5+ years in applied AI/ML system delivery.
• Experience in productionizing AI/ML models and building full-stack AI applications in enterprise settings.

Technical Skills

• Strong Python development skills; proficiency in ML/AI frameworks (PyTorch,

TensorFlow, Scikit-learn).

• Strong understanding of LLMs, RAG pipelines, vector databases (Weaviate, Qdrant, Pinecone).
• Experience with MLOps/LLMOps tools: MLflow, Argo, KServe, Feast, Kubeflow.
• Proficiency in data pipeline engineering using Spark, Airflow, or DataFlow.
• Exposure to agent orchestration frameworks: LangChain, LangGraph, AutoGen, CrewAI is a big plus.

Cloud & Infrastructure

• Hands-on experience with GCP (Vertex AI, BigQuery, Document AI, AI Gateway) and/or Azure (Azure ML, OpenAI, Synapse).
• Expertise in containerization (Docker) and orchestration (Kubernetes).
• Familiarity with Infrastructure as Code (Terraform, Pulumi, CDK).

Soft Skills

• Strong architectural thinking and problem-solving in fast-paced delivery environments.
• Excellent communication and collaboration skills to work across cross-functional teams and clients.
• Proactive, structured, and detail-oriented with a bias for execution.

Nice to Have

• Experience in real-world deployments of Agentic AI systems or collaborative multi-agent setups.
• Exposure to regulatory/ethical concerns in AI such as fairness, transparency, or bias mitigation.
• Familiarity with AI observability, explainability, and governance tooling (e.g., Arize, Fiddler, TruEra).

What We Offer

• Work on high-impact AI and Agentic AI projects across global enterprise clients.
• Opportunity to be part of a deep-tech delivery team and lead cutting-edge implementations.
• Continuous learning opportunities through workshops, certification support, and technical mentoring.