Artificial Intelligence Engineer

Artificial Intelligence Engineer & Machine Learning Engineer

Position Overview:

We are seeking an experienced AI & Machine Learning Engineer to design, build, and deploy intelligent systems that drive measurable business impact. You will own the end-to-end ML lifecycle from data ingestion and model development to production deployment and ongoing monitoring. Working alongside product, data, and engineering teams, you will translate complex business problems into scalable, reliable AI-powered solutions.

Key Responsibilities:

Model Development & Research

Design, develop, and evaluate production-grade ML, Computer vision and Small, Large language models — classification, regression, NLP, Computer Vision, and Generative AI.
Fine-tune and optimize pre-trained large language models for domain-specific use cases.
Research, prototype, and apply state-of-the-art techniques including transformers, diffusion models, and multi-modal AI.

Generative AI & LLM Engineering

Build LLM-powered applications using Agent skills, Guardrails, Agent harness engineering, MCP, Function tooling. Build agents using one or more of the following :
OpenAI Agents SDK, Claude Agents SDK, Langchain Deep Agents, Haystack connectors.
Implement safety and guardrail layers: prompt injection detection, PII redaction, content moderation, and policy enforcement.
Create evaluation harnesses and automated benchmarking pipelines for continuous LLM quality assurance.

Data Engineering & Pipelines

Build and maintain scalable, fault-tolerant data pipelines for model training, validation, and real-time inference.
Design and manage feature stores, data versioning, and preprocessing workflows for large-scale datasets.
Collaborate with data engineers to ensure data quality, consistency, and governance standards.

Deployment & MLOps

Deploy and serve ML/AI models via REST APIs and microservices using Docker, Kubernetes, and cloud platforms (AWS, GCP, Azure).
Implement CI/CD pipelines for model training, testing, and deployment using tools like MLflow, Weights & Biases, or Kubeflow.
Monitor production model performance — tracking accuracy drift, data drift, latency, and system health with alerting pipelines.
Optimize models for production: quantization, distillation, ONNX export, and hardware-specific inference tuning.

Collaboration & Leadership

Partner with product managers, business stakeholders, and domain experts to define AI solution requirements and success metrics.
Mentor junior engineers and data scientists; contribute to code reviews, architecture discussions, and technical documentation.
Communicate complex AI concepts and findings clearly to both technical and non-technical audiences.
Stay current with latest AI/ML research; evaluate and advocate for relevant new tools, frameworks, and methodologies.

Requirements:

Technical

Skills:

Proficiency in Python.

Experience with machine learning frameworks like Tensor Flow or PyTorch.
Ability to work with large datasets and expertise in data preprocessing and collection techniques.
Familiarity with language models and generative AI is highly desirable.
Knowledge of deep learning architectures including CNNs, RNNs, and Transformer-based models.

Experience with NLP techniques, text classification, named entity recognition, and sentiment analysis.
Proficiency in SQL and No

SQL databases for data storage and retrieval.
Hands-on experience with MLOps tools such as MLflow, Weights & Biases, or Kubeflow for model lifecycle management.
Familiarity with cloud platforms (AWS, GCP, or Azure) for model deployment and scaling.

Experience with containerization and orchestration tools — Docker and Kubernetes.
Knowledge of REST API development and integrating ML models into production applications.
Understanding of data versioning, feature stores, and pipeline automation best practices.

Experience:

Minimum 3 years of hands-on experience in ML/AI engineering roles, building and deploying production-grade models.
Demonstrated ownership of the full ML lifecycle — data preparation, model development, deployment, monitoring, and iteration.
Proven experience with NLP or Computer Vision solutions using modern architectures (BERT, GPT, YOLO, Res Net, etc.).
Practical experience integrating and fine-tuning large language models for real-world applications.
Experience deploying ML systems on at least one major cloud platform (AWS Sage Maker, GCP Vertex AI, or Azure ML).

Soft Skills:

Strong analytical and problem-solving mindset with meticulous attention to detail.
Excellent communication skills — able to translate technical concepts for diverse audiences.
Collaborative team player with experience in Agile/Scrum environments.
Self-motivated and intellectually curious; proactive in proposing new ideas and improvements.

What We Offer:

Competitive salary package.
Opportunity to work with international clients and projects.
Exposure to AI-driven development processes.
Professional growth and learning environment.
Friendly, collaborative work culture.