AI Data Solutions Engineer

Replicon; Bengaluru; On-site; INR 1,200,000 - 2,400,000; Full time

Job summary

Deltek is seeking a highly motivated AI Data Solutions Engineer to design, build, deploy, and optimize data foundations for AI and machine learning initiatives. You will work across batch and real-time pipelines, lakehouse architectures, and embedding pipelines to power RAG and semantic search.

You will collaborate with AI data scientists and engineering teams to deliver secure, scalable AI solutions, with production-grade applications on cloud platforms and robust data governance practices.

Qualifications

• Experience building data pipelines (ETL/ELT) and data foundations for AI workloads.
• Hands-on with ML/DL development, evaluation, and deployment.
• Familiarity with MLOps/LLMOps, data governance, and secure AI.
• Proficiency in Python and cloud-native architectures.

Responsibilities

• Design, develop, deploy, and optimize AI data pipelines and governance.
• Build and productionize AI models and intelligent solutions.
• Develop Generative AI solutions using leading LLMs and RAG techniques.
• Implement MLOps/LMMOps and data validation across platforms.
• Collaborate with data scientists, architects, and product teams.

Skills

Python

Cloud

MLOps

LLMOps

Data engineering

Generative AI

LLMs

NLP

Kubernetes

Databricks

Snowflake

APIs

CI/CD

Tools

Azure

AWS

Google Cloud

Snowflake

Databricks

Kubernetes

Job description

We are seeking a highly motivated AI Data Solutions Engineer to join Deltek s growing AI Center of Excellence. In this role, you will build the data foundations that make AI work and the intelligent solutions that run on top of them. You will design, develop, deploy, and optimize internal Artificial Intelligence and Machine Learning solutions that solve complex business challenges.

The ideal candidate is equally comfortable building reliable data pipelines and shipping production-grade AI applications, with depth in data engineering, machine learning, Generative AI, LLMs, software engineering, cloud, and MLOps/LLMOps. You understand that great AI starts with trusted, well-governed data.

You will work closely with AI data scientists, architects, and engineering teams to deliver secure, scalable AI solutions. This role reports to the Senior AI Solutions Architect.

Key Responsibilities

• Build the Data Foundation for AI
  • Design, build, and maintain scalable batch and real-time data pipelines (ETL/ELT) that ingest data from enterprise applications, APIs, files, event streams, and third-party sources.
  • Design and evolve the lakehouse, warehouse, and data models that serve analytics, machine learning, and Generative AI workloads.
  • Prepare structured and unstructured data for AI through parsing, chunking, enrichment, and metadata management, and build the embedding pipelines and vector stores that power RAG and semantic search.
  • Create reusable datasets, feature pipelines, and evaluation datasets.
• Develop Intelligent AI Solutions
  • Design, build, train, evaluate, and deploy machine learning and deep learning models.
  • Develop Generative AI solutions using LLMs such as GPT, Claude, Gemini, Llama, and Mistral, applying RAG, prompt engineering, fine-tuning, and AI agent frameworks grounded in high-quality enterprise data.
  • Build NLP, recommendation, forecasting, predictive analytics, and intelligent automation solutions.
  • Optimize model performance, scalability, latency, and cost.
• Engineer Production-Grade Applications on the Cloud
  • Develop production-grade AI applications, APIs, and microservices using Python and modern software engineering practices, and integrate them with enterprise systems and data platforms.
  • Apply coding standards, automated testing, CI/CD, and version control across data and application code.
  • Build on Azure, AWS, or Google Cloud using cloud-native data and AI services, containerization, Kubernetes, and serverless technologies.
  • Use platforms such as Snowflake and Databricks to deliver performant, cost-aware solutions for enterprise workloads, including real-time inference.
• Operate Data and AI Platforms Reliably
  • Design and implement MLOps, LLMOps, and DataOps pipelines, automating data validation, model training, testing, and deployment.
  • Monitor pipeline health, data quality and freshness, model performance, drift, and hallucinations, and act quickly on issues.
  • Support the continuous improvement, reliability, and cost efficiency of the data and AI platforms.
• Govern, Secure, and Protect
  • Ensure compliance with Responsible AI, security, privacy, and regulatory requirements across data and AI solutions.
  • Implement data cataloging, lineage, classification, retention, and access controls for data used in AI.
  • Apply model governance, explainability, bias mitigation, and risk management practices.
• Collaborate and Contribute
  • Partner with AI data scientists, architects, product teams, and business stakeholders to turn business problems into data and AI solutions, and share best practices across the AI Center of Excellence.