Job Description

We are looking for an experienced and results-driven

AI Developer

to join our team. The ideal candidate will be responsible for developing, deploying, and optimizing AI and machine learning models to solve real-world business problems. You will collaborate with cross-functional teams to deliver scalable and ethical AI solutions using state-of-the-art tools and technologies.

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Key Responsibilities

Data Engineering & Preprocessing

Collaborate with data scientists and engineers to source, clean, and preprocess large datasets. Perform feature engineering and data selection to improve model inputs.

AI Model Development & Implementation

Design, build, and validate machine learning and deep learning models, including:

+ Convolutional Neural Networks (CNNs)
+ Recurrent Neural Networks (RNNs/LSTMs)
+ Transformers
+ NLP and computer vision models
+ Reinforcement learning agents
+ Classical ML techniques
Develop models tailored to domain-specific business challenges.

Performance Optimization & Scalability

Optimize models for performance, latency, scalability, and resource efficiency. Ensure models are production-ready for real-time applications.

Deployment, MLOps & Integration

Build and maintain MLOps pipelines for model deployment, monitoring, and retraining. Use Docker, Kubernetes, and CI/CD tools for containerization and orchestration. Deploy models on cloud platforms (AWS, Azure, GCP) or on-premise infrastructure. Integrate models into systems and applications via APIs or model-serving frameworks.

Testing, Validation & Continuous Improvement

Implement testing strategies like unit testing, regression testing, and A/B testing. Continuously improve models based on user feedback and performance metrics.

Research & Innovation

Stay up to date with AI/ML advancements, tools, and techniques. Experiment with new approaches to drive innovation and competitive advantage.

Collaboration & Communication

Work closely with engineers, product managers, and subject matter experts. Document model architecture, training processes, and experimental findings. Communicate complex technical topics to non-technical stakeholders clearly.

Ethical AI Practices

Support and implement ethical AI practices focusing on fairness, transparency, and accountability.
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Required Qualifications & Skills

Core Technical Skills

Proficient in Python and experienced with libraries such as TensorFlow, PyTorch, Keras, Scikit-learn. Solid understanding of ML/DL architectures (CNNs, RNNs/LSTMs, Transformers). Skilled in data manipulation using Pandas, NumPy, SciPy.

MLOps & Deployment Experience

Experience with MLOps tools like MLflow, Kubeflow, DVC. Familiarity with Docker, Kubernetes, and CI/CD pipelines. Proven ability to deploy models on cloud platforms (AWS, Azure, or GCP).

Software Engineering & Analytical Thinking

Strong foundation in software engineering: Git, unit testing, and code optimization. Strong analytical mindset with experience working with large datasets.

Communication & Teamwork

Excellent communication skills, both written and verbal. Collaborative team player with experience in agile environments.
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Preferred

Advanced AI & LLM Expertise

Hands-on experience with LLMs (e.g., GPT, Claude, Mistral, LLaMA). Familiarity with prompt engineering and Retrieval-Augmented Generation (RAG). Experience with LangChain, LlamaIndex, and Hugging Face Transformers. Understanding of vector databases (e.g., Pinecone, FAISS, Weaviate).

Domain-Specific Experience

Experience applying AI in sectors like healthcare, finance, retail, manufacturing, or customer service. Specialized knowledge in NLP, computer vision, or reinforcement learning.

Academic & Research Background

Strong background in statistics and optimization. * Research publications in top AI/ML conferences (e.g., NeurIPS, ICML, CVPR, ACL) are a plus.