Cloud AI & Data Engineer

Acesoft Labs; Bengaluru; Hybrid; INR 2,800,000 - 5,500,000; Full time

Job summary

Acesoft Labs seeks an experienced Cloud AI and Data Engineer to design and deliver cloud-native data platforms and GenAI applications. You will deploy scalable ML models, integrate GenAI across AWS, Azure, and GCP, and collaborate with data scientists and product teams to operationalize AI-driven insights.

The role emphasizes building end-to-end GenAI capabilities, governance, and robust CI/CD for data and AI solutions within a cloud-first environment.

Qualifications

• 6–10 years of experience in data engineering or ML engineering in cloud environments (AWS, Azure or GCP).
• Proficiency in Python and SQL; familiarity with PySpark, Java, or Scala is a plus.
• Experience with GenAI models via cloud services (Bedrock, Azure OpenAI, HuggingFace).

Responsibilities

• Build and maintain scalable data pipelines and infrastructure for AI and analytics using cloud-native tools.
• Design and implement production-ready GenAI applications across cloud services (Bedrock, Azure OpenAI, Vertex AI).
• Develop and deploy AI/ML models including transformer-based and LLM solutions.
• Integrate GenAI with enterprise workflows using APIs and RAG patterns.
• Collaborate with data scientists and platform teams to operationalize AI-driven insights.

Skills

Cloud engineering

AI/ML engineering

Generative AI

Python

SQL

PySpark

MLOps

Docker

Kubernetes

AWS

Azure

GCP

FAISS

Pinecone

RAG

APIs

LangChain

SageMaker Pipelines

Vertex AI

Bedrock

BigQuery

Snowflake

Tools

Bedrock

Azure OpenAI

Vertex AI

MLflow

LangChain

SageMaker Pipelines

Weights & Biases

Docker

Kubernetes

Snowflake

BigQuery

Redshift

S3

Job description

Cloud, Artificial Intelligence, Data Engineering

Skill to Evaluate :

Cloud, Artificial Intelligence, Data Engineering

Experience :

6 to 10 Years

Location :

Bengaluru

Job Description :

Job Summary:

We are looking for an experienced Cloud AI and Data Engineer with a strong background in cloud-native data solutions, AI/ML engineering, and emerging Generative AI (GenAI) technologies. The ideal candidate will have 68 years of hands‑on experience in building robust data platforms, deploying scalable ML models, and integrating GenAI solutions across cloud environments.

Key Responsibilities:

• Build and maintain scalable data pipelines and infrastructure for AI and analytics using cloud-native tools (e.g., AWS Glue, Azure Data Factory, GCP Dataflow)
• Design and implement production‑ready GenAI applications using services like Amazon Bedrock, Azure OpenAI, or Google Vertex AI
• Develop and deploy AI/ML models including transformer‑based and LLM (Large Language Model) solutions
• Integrate GenAI with enterprise workflows using APIs, orchestration layers, and retrieval‑augmented generation (RAG) patterns
• Collaborate with data scientists, product managers, and platform teams to operationalize AI‑driven insights and GenAI capabilities
• Build prompt engineering frameworks, evaluate output quality, and optimize token usage and latency for GenAI deployments
• Set up monitoring, drift detection, and governance mechanisms for both traditional and GenAI models
• Implement CI/CD pipelines for data and AI solutions with automated testing and rollback strategies
• Ensure cloud solutions adhere to data privacy, security, and regulatory compliance standards

Required Skills & Qualifications:

• 68 years of experience in data engineering or machine learning engineering in cloud environments (AWS, Azure, or GCP)
• Proficiency in Python and SQL; familiarity with PySpark, Java, or Scala is a plus
• Experience working with GenAI models such as GPT, Claude, or custom LLMs via cloud services (e.g., Bedrock, Azure OpenAI, HuggingFace)
• Hands‑on with prompt design, fine‑tuning, vector stores (e.g., FAISS, Pinecone), and knowledge base integrations
• Experience with MLOps and LLMOps tools (e.g., MLflow, LangChain, SageMaker Pipelines, Weights & Biases)
• Solid understanding of containerization (Docker), orchestration (Kubernetes), and microservices
• Knowledge of data lake/warehouse platforms such as S3, Snowflake, BigQuery, or Redshift
• Familiar with governance frameworks, access control, and responsible AI practices

Preferred Qualifications:

• Certifications in Cloud AI/ML platforms (e.g., AWS Certified Machine Learning, Azure AI Engineer)
• Experience building RAG systems, vector database search, and multi‑turn conversational agents
• Exposure to real‑world GenAI use cases like code generation, chatbots, document summarization, or knowledge extraction
• Knowledge of OpenAPI, JSON schema validation, and API lifecycle tools