As a full spectrum Azure integrator, we assist hundreds of companies to realize the value, efficiency, and productivity of the cloud. We take customers on their journey to enable, operate, and innovate using cloud technologies – from migration strategy to operational excellence and immersive transformation.

If you like a challenge, you’ll love it here, because we’re solving complex business problems every day, building and promoting great technology solutions that impact our customers’ success. The best part is, we’re committed to you and your growth, both professionally and personally.

Job Overview:

As a Machine Learning Engineer, you will deliver ML models and pipelines that solve real-world business problems, while leveraging ML Ops best practices, to ensure successful deployment of ML models and application code. You will leverage cloud-based architectures and technologies to deliver optimized ML models at scale. You will use programming languages like Python and work with popular ML frameworks like Scikit Learn, Tensorflow etc..

If you get a thrill working with cutting-edge technology and love to help solve customers’ problems, we’d love to hear from you. It’s time to rethink the possible. Are you ready?

What You’ll Be Doing:

• Generative AI (Proven Track Record Required)
• Designing, fine-tuning, and deploying LLMs in production environments
• Building and integrating AI-powered chatbots, virtual assistants, and Retrieval-Augmented Generation (RAG) pipelines
• Expertise in prompt engineering, embeddings, and vector databases (e.g., Pinecone, Weaviate, Chroma)
• Hands-on experience with Hugging Face Transformers, LangChain, Azure OpenAI, or OpenAI API
• Demonstrated success delivering Generative AI solutions for real-world business applications

• Traditional ML & Deep Learning
• Strong background in supervised and unsupervised learning (Scikit-learn, XGBoost, LightGBM, etc.)
• Deep learning experience with CNNs, RNNs, Transformers using PyTorch or TensorFlow
• Applied experience in computer vision, NLP, or recommendation systems
• Ability to select and implement the appropriate ML approach based on business needs

• Engineering & Cloud
• Strong software engineering skills (Python, Scala, or Java) with a focus on clean architecture and testing practices
• Experience building scalable ETL/data pipelines
• Familiarity with ML Ops for CI/CD, deployment, and monitoring
• Cloud experience (Azure preferred; AWS/GCP acceptable)

Responsibilities:

• Deliver production-ready AI/ML solutions from concept to deployment.
• Lead the design and implementation of Generative AI applications.
• Apply deep learning and traditional ML methods as appropriate.
• Build scalable, cloud-native AI services and APIs.
• Implement ML Ops pipelines for automation, testing, and monitoring.
• Collaborate with product, engineering, and infrastructure teams.
• Mentor teams on best practices for both traditional ML and Generative AI.

Qualifications:

• Proven track record in delivering Generative AI/LLM solutions in production environments.
• Minimum 4 years of programming experience with Python, Scala, or Java.
• At least 2 years of hands-on experience with Generative AI or conversational AI projects.
• Experience deploying traditional ML and deep learning models at scale.
• Strong experience with PyTorch, TensorFlow, Scikit-learn, and relevant AI libraries.
• Familiarity with vector search, embeddings, and semantic search architectures.
• Understanding of ML Ops best practices for production deployments.