AI/ML Architects

Genetic Callnet Consultancy Pvt Ltd.; Maharashtra; On-site; INR 3,000,000 - 6,000,000; Full time

Job summary

Genetic Callnet Consultancy Pvt Ltd. in Mumbai seeks an experienced AI/ML Architect to lead end-to-end design and delivery of enterprise AI/ML, Generative AI and Agentic AI solutions onsite in Mumbai. You will own architecture, delivery, and governance while guiding engineering teams and collaborating with clients and data science teams.

You will drive LLMs, RAG, vector databases and orchestration, build robust MLOps/LLMOps pipelines and ensure secure scalable deployments across enterprises.

Qualifications

• 10–12 years of software/architecture experience with at least 5 years in AI/ML
• Experience designing and deploying production AI/ML solutions
• Strong hands-on Python development and AI/ML toolchains
• Exposure to enterprise AI governance, security and scalability

Responsibilities

• Own end-to-end architecture and delivery of AI/ML and Agentic AI solutions
• Translate designs into scalable, secure production apps
• Lead engineering teams, review designs and ensure quality
• Drive LLM, RAG, vector DBs, model training and orchestration
• Establish MLOps/LLMOps pipelines, monitoring and governance
• Collaborate with clients, data science teams and stakeholders

Skills

Python

AI/ML architecture

LLMs

RAG

Vector databases

LangChain

LangGraph

MLOps

LLMOps

Docker

Kubernetes

GCP

Azure

Spark

Airflow

PyTorch

TensorFlow

Scikit-learn

MLflow

Kubeflow

KServe

Argo

Spark

Tools

PyTorch

TensorFlow

Scikit-learn

MLflow

Kubeflow

KServe

Argo

Docker

Kubernetes

Spark

Airflow

Job description

We are looking for experienced AI/ML Architects to lead the design and delivery of enterprise AI/ML, Generative AI and Agentic AI solutions.

If you enjoy combining hands-on engineering with solution architecture, technical leadership and solving complex business challenges, we'd love to hear from you.

• Experience: 10–12 Years
• Location: Mumbai – Lower Parel, BKC, Malad or Kalina-Santacruz
• Work Mode: Onsite | 5 Days a Week

Key Responsibilities

• Own end-to-end architecture and technical delivery of enterprise AI/ML and Agentic AI solutions.
• Translate solution designs into scalable, secure and production-ready applications.
• Lead engineering teams, conduct design and code reviews, and ensure quality across implementation and deployment.
• Drive LLM, RAG, vector database, model training, fine-tuning and AI orchestration initiatives.
• Establish MLOps/LLMOps practices, deployment pipelines, monitoring and AI governance.
• Work closely with clients, data science teams and business stakeholders to deliver enterprise-grade solutions.

Technical Skills Required

• Strong hands-on programming expertise in Python.
• Strong understanding of AI/ML architecture, LLMs, Generative AI, RAG and Agentic AI.
• Experience with PyTorch, TensorFlow or Scikit-learn.
• Familiarity with vector databases such as Pinecone, Qdrant or Weaviate.
• Experience with frameworks such as LangChain, LangGraph, AutoGen or CrewAI.
• MLOps/LLMOps exposure to tools such as MLflow, Kubeflow, KServe or Argo.
• Working knowledge of Docker, Kubernetes and data pipeline tools such as Spark or Airflow.

Ideal Candidate

We'd love to connect with professionals who:

• Have 10+ years of experience in software engineering or architecture, with at least 5 years in applied AI/ML.
• Have successfully designed and deployed AI/ML solutions in production environments.
• Can balance hands-on technical expertise with architecture ownership and team leadership.
• Have experience delivering enterprise AI solutions and engaging directly with clients.
• Understand scalability, security, responsible AI, governance and enterprise integration.

Good to Have: Experience with multi-agent AI systems, AI observability platforms (Arize, Fiddler, TruEra) and enterprise AI governance.

Must have Skills

AI Architecture | Artificial Intelligence | Machine Learning | LLM | Python | RAG | Vector Databases | Agentic AI | LangChain | LangGraph | MLOps | LLMOps | GCP | Azure | Docker | Kubernetes

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