AI/ML Engineer

We are looking for a highly skilled AI/ML Engineer who can design, build, and deploy intelligent systems that power nextgeneration enterprise solutions. This role is ideal for someone who thrives in a fastmoving environment, enjoys solving complex problems, and wants to contribute to scalable, globalimpact products.

Key Responsibilities

Machine Learning & AI Development:

Build, train, and optimize ML models for classification, prediction, NLP, and generative AI use cases.

Develop scalable pipelines for data ingestion, preprocessing, feature engineering, and model deployment.

Implement LLMbased solutions, including finetuning, prompt engineering, and retrievalaugmented generation (RAG).

Engineering & Architecture:

Design and maintain productiongrade ML systems using cloud platforms (AWS, Azure, GCP).

Build APIs, microservices, and automation workflows to integrate ML models into enterprise applications.

Ensure models meet performance, reliability, and security standards.

Data & Analytics:

Work with structured and unstructured datasets at scale.

Develop monitoring systems for model drift, data quality, and performance metrics.

Collaborate with data engineers and product teams to translate business needs into technical solutions.

Research & Innovation:

Stay current with advancements in AI/ML, including LLMs, transformers, vector databases, and MLOps best practices.

Prototype new ideas and evaluate emerging tools, frameworks, and architectures.

Required Skills & Experience:

37 years of experience in AI/ML engineering or applied data science.

Strong proficiency in Python and ML frameworks (TensorFlow, PyTorch, Scikitlearn).

Experience with LLMs, embeddings, vector stores (FAISS, Pinecone, Chroma), and RAG pipelines.

Handson experience with cloud services (AWS Sagemaker, Azure ML, GCP Vertex AI).

Solid understanding of algorithms, data structures, and distributed systems.

Experience deploying ML models into production environments.

Familiarity with CI/CD, Docker, Kubernetes, and MLOps workflows.

Preferred Qualifications:

Experience with generative AI (LLMs, diffusion models, multimodal systems).

Knowledge of bigdata tools (Spark, Databricks, Kafka).

Exposure to enterprise software domains such as ERP, supply chain, or financial systems.

Contributions to opensource AI/ML projects.

Soft Skills:

Strong problemsolving and analytical thinking.

Excellent communication and documentation skills.

Ability to work independently in a remote, distributed team.

Ownership mindset with a passion for innovation.