AI/ML Engineer - Generative AI and Multimodal Systems

About Spekond

Spekond is dedicated to delivering cutting-edge technological solutions while valuing the significance of a harmonious work-life balance. The company emphasizes motivation and productivity by integrating innovation with well-being.

Role Overview

We are seeking a seasoned AI/ML Engineer to lead and enhance our foundational AI systems. The candidate will have extensive practical experience with Generative AI, Natural Language Processing (NLP), and scalable machine learning platforms used in production environments. The primary focus will be on sustaining, refining, and expanding the AI infrastructure that supports our child development assessment solutions.

Key Responsibilities

• Architect and implement full AI workflows capable of processing multimodal data such as video, audio, images, and text.
• Develop and manage recommendation algorithms, neural networks, and large language models (LLMs).
• Integrate Large Language Models and Vision Language Models to enable insightful data extraction and reasoning capabilities.
• Design and sustain Retrieval-Augmented Generation (RAG) workflows to produce accurate and contextual AI outputs.
• Create and coordinate agentic AI sequences for handling complicated multi-phase automation tasks.
• Construct recommendation systems and user personalization based on behavioral pattern analysis and segmentation.
• Deploy AI models in cloud production environments ensuring continuous performance monitoring and assessment.
• Expose AI functionalities through RESTful APIs to support product integrations.
• Conduct exploratory data analysis to glean insights aiding product strategy.
• Collaborate with diverse teams to convert business needs into AI-centric solutions.
• Develop rigourous evaluation and testing methods to guarantee model performance, dependability, and safety.
• Keep abreast of emerging trends and breakthroughs in LLMs, multimodal AI, and agentic system design.

Qualifications and Experience

• Expertise in building RAG pipelines incorporating techniques such as document segmentation, embedding strategies, and vector database use.
• Proven work with Vision Language Models for interpreting various input formats.
• Advanced prompt engineering capabilities including system design, few-shot learning, chain-of-thought reasoning, and structured output design.
• Familiarity with orchestration frameworks like LangChain and LangGraph for agentic AI workflows.
• Experience integrating multiple LLM APIs and constructing multi-model pipelines.
• Understanding strategies to reduce hallucinations and ensure grounded, reliable outputs in deployment environments.
• Strong foundation in NLP tasks such as text classification, sequence prediction, and working with transformer-based models.
• Experience fine-tuning pretrained language models.
• Hands-on knowledge of graph learning and relational modeling techniques.
• Competence in managing multi-label and multi-class classification challenges.
• Technical skills in PyTorch, TensorFlow, and Keras frameworks.
• Experience leveraging MongoDB for storing, querying, and integrating data into AI pipelines.
• Proficiency in Docker for containerizing machine learning services.
• Familiarity with cloud serverless deployment platforms, specifically Google Cloud Run.
• Setting up and managing CI/CD pipelines using tools like GitHub Actions.
• Developing RESTful APIs using frameworks such as FastAPI for model deployment.
• Model version control, monitoring, and maintenance in production environments.

Desired Candidate Profile

• At least 6 years of practical AI/ML engineering experience.
• Strong analytical skills and ability to solve challenging, open-ended problems.
• Proven experience in transitioning AI research or proofs-of-concept into full production systems.
• Self-motivated with a strong sense of ownership.
• Excellent communication abilities.

Eligibility

Applicants must be graduates from any discipline.