We are looking for a Senior AI/ML Engineer with 5+ years of hands-on experience designing, building, and deploying machine learning and AI-driven systems in production. You will work across the full ML lifecycle from data pipelines and model development to deployment and monitoring partnering closely with product, engineering, and client teams to deliver intelligent solutions that create measurable business impact.

KEY RESPONSIBILITIES

– Design, develop, and deploy machine learning models and AI-driven features for production applications.

– Build and maintain data pipelines for training, evaluation, and inference, ensuring data quality and reproducibility.

– Fine-tune and integrate large language models (LLMs) and other foundation models into client-facing products.

– Design and implement retrieval-augmented generation (RAG) pipelines, vector search, and prompt engineering strategies.

– Develop and expose model inference APIs, ensuring low latency, scalability, and reliability.

– Collaborate with backend and mobile engineering teams to integrate AI capabilities into existing platforms.

– Establish MLOps practices, including model versioning, CI/CD for ML, and automated monitoring for drift and performance degradation.

– Evaluate model outputs for accuracy, bias, and safety, and implement guardrails where required.

– Optimize model performance, inference cost, and compute utilization across cloud environments.

– Stay current with emerging AI/ML research and tooling, and recommend adoption where it adds client value.

– Document architecture, experiments, and model decisions to support internal knowledge sharing and client delivery.

REQUIRED SKILLS & QUALIFICATIONS

– 5+ years of professional experience in machine learning, deep learning, or applied AI engineering.

– Strong proficiency in Python and ML frameworks such as PyTorch, TensorFlow, or scikit-learn.

– Hands-on experience working with LLMs (OpenAI, Anthropic, open-source models) via APIs or self-hosted deployment.

– Practical experience with RAG architectures, embeddings, and vector databases (e.g., Pinecone, Weaviate,

FAISS, pgvector).

– Solid understanding of the ML lifecycle: data preprocessing, feature engineering, training, evaluation, and deployment.

– Experience deploying models as REST/GraphQL services using frameworks such as FastAPI or Flask.

– Familiarity with cloud AI/ML platforms (AWS SageMaker, Azure ML, or GCP Vertex AI).

– Working knowledge of containerization and orchestration (Docker, Kubernetes) for model deployment.

– Experience with MLOps tooling such as MLflow, Weights & Biases, or similar for experiment tracking.

– Strong understanding of prompt engineering, fine-tuning, and model evaluation techniques.

– Proficiency with Git-based version control and collaborative development workflows.

– Strong analytical and problem-solving skills, with the ability to work independently in a client-facing, fast- paced environment.

GOOD TO HAVE

– Experience with agentic AI frameworks (LangChain, LlamaIndex, or similar).

– Exposure to fine-tuning open-source LLMs and parameter-efficient techniques (LoRA, QLoRA).

– Familiarity with data engineering tools such as Airflow, Spark, or Kafka.

– Understanding of AI governance, responsible AI practices, and data privacy considerations.

– Prior experience in an AI consulting or client-delivery environment, managing multiple concurrent projects.

– Exposure to fintech, mobile, or cloud-platform domains.