AIML Engineer

Job Description

We are looking for an experienced AI/ML Engineer to design, build, and support machine learning and AI solutions for enterprise applications. The ideal candidate will have strong hands-on experience with Python, machine learning, data engineering, and modern AI technologies, along with the ability to take solutions from development through production.

This role will work closely with data scientists, software engineers, product teams, and business stakeholders to identify opportunities where AI and machine learning can solve business problems and deliver measurable value.

Key Responsibilities

• Design, develop, and deploy machine learning and AI solutions for business and enterprise use cases.
• Build and maintain machine learning models using Python and commonly used ML frameworks.
• Work with large and complex datasets to perform data preparation, feature engineering, model development, and evaluation.
• Develop scalable data and ML pipelines for training, testing, deployment, and monitoring.
• Apply machine learning techniques including supervised and unsupervised learning, classification, regression, clustering, NLP, and predictive analytics.
• Work with Generative AI and LLM-based solutions, including RAG, embeddings, vector databases, prompt engineering, and model integration .
• Develop APIs and services to integrate AI/ML models with enterprise applications.
• Deploy and manage ML solutions in cloud environments such as AWS, Azure, or GCP .
• Implement MLOps practices including model versioning, CI/CD, model monitoring, testing, and production support.
• Collaborate with data scientists and engineering teams to improve model performance, scalability, and reliability.
• Analyze model results and business requirements and translate them into practical AI solutions.
• Troubleshoot production issues and continuously improve existing models and AI applications.
• Follow appropriate security, governance, privacy, and responsible AI practices when working with enterprise and customer data.

Must Have Skills

• 8+ years of experience in software engineering, machine learning, data science, or a closely related field.
• Strong hands-on experience with Python and ML/AI development.
• Solid understanding of machine learning algorithms, statistics, model evaluation, and data preparation.
• Experience with scikit-learn, TensorFlow, PyTorch , or similar ML frameworks.
• Experience building and deploying machine learning models in production environments.
• Strong experience with SQL and working with relational and/or large-scale data platforms.
• Experience developing APIs and integrating ML/AI solutions with enterprise applications.
• Hands-on experience with at least one major cloud platform: AWS, Azure, or GCP .
• Experience with data pipelines, model deployment, monitoring, and MLOps practices.
• Good understanding of software engineering principles, version control, testing, and CI/CD.
• Strong communication skills and the ability to work with both technical and business teams.

Preferred Skills

• Experience in the financial services, banking, fintech, insurance, or investment domain.
• Experience with Generative AI, LLMs, RAG, vector databases, and AI agents .
• Experience with platforms/tools such as OpenAI, Azure OpenAI, AWS Bedrock, Vertex AI, Hugging Face, LangChain, or LlamaIndex .
• Experience with Snowflake, Databricks, Spark , or other modern data platforms.
• Knowledge of model governance, AI security, data privacy, and responsible AI practices.
• Experience working with financial datasets, risk analytics, fraud detection, credit scoring, forecasting, customer analytics, or similar financial use cases.

What We're Looking For

We’re looking for someone who is comfortable working across the full AI/ML lifecycle — from understanding the business problem and preparing data to building models, deploying them, and supporting them in production.

The right candidate should be hands-on, practical, and able to work in a fast-paced enterprise environment while collaborating with engineers, data teams, and business stakeholders.