AI Solutions Engineer

Deltek; Bengaluru; On-site; INR 2,500,000 - 4,000,000; Full time

Job summary

Deltek seeks a highly motivated AI Solutions Engineer to join its AI Center of Excellence to design, deploy, and optimize internal AI/ML solutions that address complex business challenges.

The role collaborates with AI data scientists, architects, and engineering teams to deliver innovative AI-driven applications with a focus on security, scalability, governance, and operational excellence, reporting to the Senior AI Solutions Architect.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, Engineering or related field.

Responsibilities

• Design, train, evaluate, and deploy machine learning and deep learning models.
• Develop Generative AI solutions using LLMs (GPT, Claude, Gemini, Llama, Mistral).
• Implement Retrieval-Augmented Generation (RAG), prompt engineering, fine-tuning, and AI agent frameworks.
• Build NLP, recommendation, forecasting, predictive analytics, and intelligent automation solutions.
• Optimize model performance, scalability, latency, and cost.
• Develop production-grade AI applications using Python and modern software practices.
• Build APIs, microservices, and AI-powered enterprise apps.
• Integrate AI services with enterprise systems and data platforms.
• Apply coding standards, automated testing, CI/CD, and version control.

Skills

Python programming

Algorithms & data structures

APIs & software design

Cloud computing

MLOps/LLMOps

Education

Bachelor’s or Master’s in Computer Science or related field

Tools

Python

PyTorch

TensorFlow

Scikit-learn

LangChain

LlamaIndex

Semantic Kernel

MCP

A2A

Transformers

FastAPI

Flask

Docker

Kubernetes

AWS

Azure

Google Cloud

AWS SageMaker

Job description

We are seeking a highly motivated AI Solutions Engineer to join Deltek’s growing AI Center of Excellence team to design, develop, deploy, and optimize internal Artificial Intelligence and Machine Learning solutions that solve complex business challenges. The ideal candidate combines deep expertise in AI, machine learning, Generative AI, Large Language Models (LLMs), SLMs, software engineering, cloud computing, and MLOps/LLMOps to build scalable, production-grade AI applications.

The AI Solutions Engineer will collaborate with AI data scientists, architects, and engineering teams to deliver innovative AI-driven solutions while ensuring security, scalability, governance, and operational excellence. This role reports to the Senior AI Solutions Architect.

Key Responsibilities

• Design, build, train, evaluate, and deploy machine learning and deep learning models.
• Develop Generative AI solutions using Large Language Models (LLMs) such as GPT, Claude, Gemini, Llama, and Mistral.
• Implement Retrieval-Augmented Generation (RAG), prompt engineering, fine-tuning, and AI agent frameworks.
• Build NLP, recommendation systems, forecasting, predictive analytics, and intelligent automation solutions.
• Optimize model performance, scalability, latency, and cost.

Software Engineering & Solution Development

• Develop production-grade AI applications using Python and modern software engineering practices.
• Build APIs, microservices, and AI-powered enterprise applications.
• Integrate AI services with enterprise systems, business applications, and data platforms.
• Apply coding standards, automated testing, CI/CD, and version control best practices.

MLOps & AI Operations

• Design and implement MLOps pipelines for model development, deployment, monitoring, and lifecycle management.
• Automate model training, validation, testing, and deployment processes.
• Monitor model performance, data drift, hallucinations, and operational metrics.
• Support continuous improvement and reliability of AI platforms.
• Develop AI solutions on Azure, AWS, or Google Cloud platforms.
• Leverage cloud-native AI services, containerization, Kubernetes, and serverless technologies.
• Build scalable architectures supporting enterprise AI workloads and real-time inference.

AI Governance & Security

• Ensure compliance with Responsible AI, security, privacy, and regulatory requirements.
• Implement model governance, explainability, bias mitigation, and risk management practices.
• Maintain standards for secure design, deployment, and operation of AI solutions.

Qualifications

Required Qualifications

Education

• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related technical field.

Experience

• 5+ years of software engineering or machine learning development experience.
• 2+ years of hands‑on experience developing and deploying Agentic AI, Generative AI or AI/ML solutions in production environments.

Technical Skills

• Strong expertise in Python.
• Solid understanding of algorithms, data structures, APIs, and software design principles.

Artificial Intelligence & Machine Learning

• Machine Learning and Deep Learning concepts and frameworks.
• Model training, evaluation, optimization, and deployment.

Generative AI

• Prompt Engineering
• Retrieval-Augmented Generation (RAG)
• AI Agents and Agentic Workflows
• Fine-tuning and model customization
• Vector embeddings and semantic search

Frameworks & Tools

• PyTorch, TensorFlow, Scikit-learn
• LangChain, LlamaIndex, Semantic Kernel, MCP, A2A and Transformers
• FastAPI, Flask

Data & Analytics

• SQL and NoSQL databases
• Data pipelines, ETL, and data modeling
• Experience with AWS, Azure and Google

MLOps & DevOps

• Docker and Kubernetes
• CI/CD automation and model monitoring
• AWS (preferred)
• AWS SageMaker

Preferred Qualifications

• Experience designing enterprise-scale AI platforms and products.
• Knowledge of multi-agent architectures and autonomous AI systems.
• Experience with vector databases such as Pinecone, Snowflake Cortex, Pgvector, Weaviate, Chroma, or Azure AI Search.
• Understanding of AI governance, compliance, and Responsible AI frameworks.
• Relevant certifications in Azure AI, AWS Machine Learning, or Google Cloud AI.