Senior AI / ML Engineer

Experience: 3+ years
Engagement Type: Contract
Work Mode: Hybrid

Role Overview:

We are seeking an experienced Senior AI/ML Engineer to design and deploy intelligent systems powered by large language models and modern AI tooling. The ideal candidate brings deep expertise in building end-to-end ML pipelines, working with LLM APIs, and operationalising AI-driven classification and detection systems.

Key Responsibilities:

• Design and implement multi-layer detection or classification pipelines combining rule-based and LLM-based approaches.
• Translate domain taxonomies and business requirements into structured AI/ML schemas and detection logic.
• Integrate and fine-tune LLM APIs (e.g. OpenAI, Gemini, or equivalent) for contextual understanding tasks.
• Build and validate evaluation frameworks including ground-truth labelling schemas and annotation guidelines.
• Conduct calibration sessions and threshold tuning to optimise precision, recall, and false positive rates.
• Develop audio/multimodal analysis modules using pre-trained models and feature extraction libraries.
• Benchmark AI system outputs against baseline or existing systems, and prepare comparative reports.
• Collaborate with cross-functional teams to deploy pipelines on client or constrained infrastructure.

Required Skills & Qualifications:

• 3+ years of experience in AI/ML engineering with strong Python skills.
• Hands-on experience with LLM APIs — prompt engineering, classification workflows, and output parsing.
• Solid understanding of text classification, NLP, and evaluation metrics (precision, recall, F1).
• Experience building or contributing to ML evaluation sets and ground truth datasets.
• Basic audio processing knowledge — feature extraction using libraries such as librosa or equivalent.
• Ability to work efficiently within hardware constraints (e.g. CPU-only, limited RAM environments).
• Familiarity with coding agents and AI-assisted development workflows.

Good to Have:

• Experience with multilingual content, particularly Indian languages alongside English.
• Exposure to voice/tone analysis models for aggression or sentiment detection.
• Familiarity with model calibration and human-in-the-loop annotation workflows.