Required Skills

Python, Artificial Intelligence, (FASB) Financial Accounting Standards Board, FAST API, React, API (Application Programming Interface), Machine Learning, Deep Learning, MLOps: Docker . Mlflow . ClearML, Deployment; Docker, Google Cloud, CI/CD Pipeline, Kubernetes, AWS, GCP, MySQL, Databases, Langchain, PyTorch, TensorFlow

Job Description

We are seeking skilled and motivated AI Engineering professionals to build, deploy, and optimize AI-powered applications, machine learning models, LLM workflows, automation systems, and scalable AI infrastructure.

The ideal candidates should have strong knowledge of Python, AI/ML systems, LLM integration, APIs, backend development, cloud infrastructure, databases, MLOps, and modern software development practices.

This role is suitable for candidates interested in working across AI product development, machine learning model deployment, prompt engineering, agentic workflows, RAG pipelines, DevOps/MLOps, and production-ready AI systems.

Key Responsibilities

Build and integrate AI-powered applications into software products.

Design, train, deploy, and optimize machine learning models.

Develop scalable backend systems for AI-heavy features.

Build and optimize LLM workflows, prompts, and RAG pipelines.

Develop agentic workflows using tool-calling, SQL, and multi-step reasoning.

Design and maintain MLOps and CI/CD pipelines for AI model lifecycle management.

Manage cloud-native infrastructure using AWS, Azure, or GCP.

Deploy containerized AI workloads using Docker and Kubernetes.

Monitor model performance, infrastructure health, data drift, and system reliability.

Optimize model serving, inference latency, and application performance.

Build data pipelines for cleaning, preprocessing, and managing high-volume datasets.

Implement AI security, data privacy, and reliability standards.

Collaborate with product, engineering, and business teams to deliver AI-driven solutions.

Job Specification

Strong knowledge of Python.

Experience with AI/ML, LLMs, or AI application development.

Experience with FastAPI, Node.js, or similar backend frameworks.

Knowledge of API design and system architecture.

Understanding of machine learning model development, training, or deployment.

Experience with databases, SQL, and data management.

Familiarity with Docker, Kubernetes, CI/CD, or cloud-based deployment.

Understanding of MLOps or model lifecycle management.

Ability to build scalable, secure, and production-ready AI systems.

Strong problem-solving, debugging, and analytical skills.

Ability to work collaboratively with technical and non-technical teams.

Preferred Skills

Experience with PyTorch, TensorFlow, Scikit-learn, Pandas, or NumPy.

Knowledge of LangChain, LlamaIndex, or LLM orchestration tools.

Experience with vector databases such as Pinecone, Milvus, or similar tools.

Familiarity with RAG pipelines and LLM evaluation frameworks.

Experience with Terraform, Prometheus, Grafana, or cloud architecture.

Knowledge of model optimization techniques such as quantization, pruning, or hyperparameter tuning.

Experience with NLP, computer vision, tabular data, or deep learning architectures.

Familiarity with React or frontend integration for AI products.

Experience with agentic systems, automation workflows, or AI-native products.

Knowledge of AI safety, hallucination evaluation, and data privacy practices.