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Ai Ml Engineer

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Onsite

Ai Ml Engineer

Conqueri Technologies

Ai Ml Engineer

Skills

Google Cloud PlatformDockerPythonData SciencePyTorchTensorFlowMachine LearningAIKubernetesApache AirflowComplianceTerraformAzure

10 days ago

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Important information


Contract type:

Permanent

Location:

Hyderabad, Telangana, India

Work mode:

Onsite

Published on:

13 October 2026

What they need


About the Role

We are looking for experienced AI/ML 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, fine-tuning, 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

  • 10+ 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.
  • 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).

Interested Candidates can drop a resume on [email protected]

or Please call me on 8332951064

Salary is not a Constraint for Deserving Candidate

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