APHRC is seeking to recruit a Machine Learning/Data Engineer, s/he will develop and optimize the AI-powered applications, ensuring seamless integration of machine learning models and LLM APIs. The appointment will be for a period of six (6) months.

KEY RESPONSIBILITIES:

The Center operates on the principle that data is a public good and champions the Findable, Accessible, Interoperable, and Reusable (FAIR) principles of data management to facilitate secure, timely, and user-friendly data access and sharing, ensuring it is available to all authorized users. The Machine Learning specialist will bring essential competencies in mobile AI/ML integration, scalable backend systems, and cloud-native DevOps, positioning DSP at the forefront of AI innovation:

Education Qualifications, Experience and Core Competencies

• Integrate and deploy AI/ML models and LLMs within mobile and web applications.
• Develop, manage, and optimize Dockerized AI services.
• Ensure high availability, scalability, and security across cloud and on-prem systems.
• Automate and streamline CI/CD pipelines to facilitate seamless updates.
• Implement cost-monitoring and optimization strategies for cloud resources.
• Develop comprehensive disaster recovery plans for critical AI services.
• Maintain secure and efficient API interactions with AI models.
• Monitor, measure, and enhance API latency for real-time responsiveness.
• Document AI workflows and data pipelines to facilitate cross-team collaboration.
• Bachelor’s degree in computer science, Data Science, or a related field with specialization in AI/ML.
• Two (2) years of experience in DevOps/cloud roles, including at least one (1) year specifically dedicated to AI model deployment.
• Hands-on experience integrating ML models (TensorFlow Lite, ONNX, PyTorch) into mobile applications.
• Proficiency with Large Language Model (LLM) APIs such as OpenAI, DeepSeek, Claude, and Llama, including advanced prompt engineering techniques.
• Experience in building Retrieval-Augmented Generation (RAG) pipelines, fine-tuning, and developing agent-based systems.
• Strong familiarity with AI orchestration frameworks like LangChain, LlamaIndex, or AutoGen.
• Knowledge and practical experience with vector databases (FAISS, Chroma, Pinecone, Weaviate).
• Proficiency in Python data transformation tools, including pandas, NumPy, scikit-learn, and pyCaret.
• Familiarity with ML model deployment tools (MLflow, Seldon Core).
• Expertise with containerization technologies (Docker) and orchestration platforms (Kubernetes—EKS, GKE, AKS).
• Mastery of cloud services on AWS, GCP, Azure, or on-prem equivalents such as OpenStack.
• Strong experience in Infrastructure as Code (IaC) tools like Terraform, Ansible, or Pulumi.
• Proficient in setting up CI/CD pipelines using GitHub Actions, ArgoCD, or Jenkins.
• Familiarity with monitoring and logging solutions such as Prometheus, Grafana, and ELK Stack.
• Knowledge of model quantization and edge AI for efficient on-device inference.
• Experience with model serving platforms like TensorFlow Serving or Triton Inference Server.
• Competency in database scaling and management, specifically PostgreSQL/MySQL for AI workloads.
• Strong analytical skills to troubleshoot and solve deployment bottlenecks.
• Ability to manage and optimize cloud/on-prem infrastructure costs.
• Security-focused mindset to ensure robust, secure AI application deployments.
• Proven capability in designing scalable and efficient AI architectures.
• Excellent documentation and knowledge-sharing abilities.