AI Engineer

Job Purpose

As an AI Engineer at Tarjama&, you will be responsible for supporting the design, development, and deployment of agentic and multimodal AI solutions leveraging language, vision, and speech technologies. You will contribute to building scalable, production-ready AI products by assisting in model integration, evaluation, optimization, and data pipeline preparation. You will collaborate closely with senior

Requirements

Duties & Responsibilities

AI Agentic Workflows & Frameworks

Design and implement AI agentic workflows that orchestrate language, vision, and speech models for multi-step tasks.

Build and extend task-driven, tool-using AI agents using modern agent frameworks and orchestration patterns.

Implement decision logic, memory strategies (short-term and vector-based), and tool-calling mechanisms in production-ready systems.

Improve agent reliability through structured prompting, planning strategies, and basic error-handling mechanisms.

Multimodal AI Development (Text, Vision, Speech)

• Develop and integrate text-based AI systems, including LLM pipelines, embeddings, rerankers, and RAG architectures.
• Build document understanding pipelines using OCR and vision-based models to extract structured insights from complex documents.
• Develop and optimize speech-based AI solutions, including STT, TTS, and conversational voice interfaces.
• Design and improve multilingual and translation pipelines with attention to performance and quality.
• AI Product Development & Deployment
• Own the development of specific AI components or features from design through deployment.
• Deploy, monitor, and optimize AI systems in production with guidance on scalability and cost-efficiency.
• Collaborate with engineering teams to integrate AI components into maintainable and scalable systems.
• Implement logging, monitoring, and basic observability practices to track system performance.
• Model Evaluation & Optimization
• Define and apply evaluation metrics for AI systems, including accuracy, latency, and robustness.
• Benchmark and optimize models for inference performance and cost-efficiency.
• Run experiments and A/B tests to validate improvements and support data-driven decisions.
• Identify common failure modes and contribute to improving model robustness and reliability.

Data Processing & Pipeline Support

• Design and maintain data pipelines for text, document, and audio data across training and inference workflows.
• Ensure data quality and consistency in collaboration with data teams.
• Analyze model outputs and user interactions to support iterative system improvements.

Cross-Functional Collaboration

• Collaborate with product managers, designers, and engineers to translate requirements into scalable AI solutions
• Partner with senior engineers on system design while independently handling well-scoped components.
• Support integration, testing, and deployment of AI systems in production environments.

Documentation & Knowledge Sharing

• Maintain clear documentation for AI pipelines, agent workflows, and evaluation processes.
• Contribute to team knowledge sharing through code reviews, demos, and technical discussions.
• Follow and help reinforce best practices for AI development and deployment.

Originally posted on Himalayas