Manager

Job Description

Job Description: AI Developer

Job Summary

We are seeking a highly skilled and innovative AI Developer to design, build, and deploy artificial intelligence-driven solutions that enhance automation, decision-making, and user engagement across enterprise and consumer applications. The role involves working with machine learning (ML), natural language processing (NLP), generative AI, and conversational AI models while ensuring integration with business systems, cloud environments, and real-time applications.

The AI Developer will collaborate with cross-functional teams including solution architects, cloud engineers, data scientists, and business analysts to develop scalable AI solutions tailored to organizational needs. This role requires a combination of software engineering expertise, AI/ML model development, and applied research to create impactful and production-ready AI systems.

Key Responsibilities

1. AI/ML Model Development

- Design, train, and fine-tune machine learning and deep learning models for tasks such as classification, regression, clustering, recommendation, NLP, speech recognition, and generative AI.
- Leverage transformer-based architectures (BERT, GPT, LLaMA, etc.) for conversational AI, summarization, Q&A, and semantic search.
- Implement retrieval-augmented generation (RAG) pipelines for contextual AI applications.
- Apply reinforcement learning from human feedback (RLHF) where appropriate.

2. Conversational AI & Generative AI Solutions

- Build and maintain intelligent virtual assistants, chatbots, and agent assist platforms using frameworks like Dialogflow CX, Rasa, Microsoft Bot Framework, and LangChain.
- Integrate generative AI capabilities for content creation, knowledge retrieval, summarization, and personalized responses.
- Work with speech-to-text (STT), text-to-speech (TTS), and multimodal AI systems to enable real-time conversational experiences.

3. Data Engineering & Preprocessing

- Collect, clean, annotate, and transform datasets from structured, unstructured, and streaming sources.
- Use feature engineering, embeddings, and vector databases (e.g., ChromaDB, Pinecone, Weaviate, FAISS) for semantic search and contextual responses.
- Ensure compliance with data privacy, PII redaction, and security standards (GDPR, SOC2, ISO 27001).

4. Software Engineering & Deployment

- Develop AI-powered microservices in Python, Node.js, or Java, following best practices in modular design and CI/CD pipelines.
- Containerize and orchestrate deployments using Docker, Kubernetes, and Helm charts.
- Deploy models on cloud platforms (GCP Vertex AI, AWS Sagemaker, Azure AI) as well as on edge devices if required.
- Integrate APIs, webhooks, and real-time streaming protocols (gRPC, WebSocket, REST) for AI applications.

5. Collaboration & Research

- Work closely with solution architects, cloud architects, and QA teams to align AI solutions with business goals.
- Stay updated with the latest AI research (transformers, multimodal AI, LLM fine-tuning, knowledge distillation).
- Publish technical documentation, design papers, and internal best practices.
- Mentor junior developers and contribute to organizational AI competency building.

Required Skills

- Strong programming skills in Python (TensorFlow, PyTorch, Scikit-learn, Hugging Face).
- Knowledge of machine learning pipelines, feature engineering, and model evaluation.
- Experience with conversational AI, NLP, and generative AI frameworks.
- Proficiency in SQL/NoSQL databases and vector databases.
- Cloud experience: AWS, Azure, or GCP (AI/ML services, serverless computing, storage, and networking).
- Knowledge of DevOps and MLOps tools (GitHub Actions, Jenkins, MLflow, Kubeflow).
- Familiarity with data security, compliance, and ethical AI principles.

Preferred Skills

- Experience with multi-agent AI frameworks and autonomous agents.
- Knowledge of speech recognition, voice analytics, and emotion detection.
- Practical understanding of microservices architecture, API management
- Hands-on with Vector embeddings, RAG, and fine-tuning LLMs.
- Exposure to data annotation workflows and quality assurance in AI datasets.

Tools & Technologies

- Languages: Python, Java, Node.js
- Frameworks & Libraries: TensorFlow, PyTorch, Hugging Face Transformers, LangChain, Rasa
- Cloud Platforms: AWS SageMaker, GCP Vertex AI, Azure AI
- Databases: PostgreSQL, MongoDB, Pinecone, FAISS, ChromaDB
- CI/CD & MLOps: Jenkins, GitHub Actions, MLflow, Docker, Kubernetes
- Security & Compliance: PII Redaction, SOC2, GDPR, TLS, Key Rotation

Example Projects

1. Conversational Agent Assist - Built a real-time transcription and recommendation engine integrated with Five9/Genesis telephony and Dialogflow CX.
2. RAG-Powered Knowledge Bot - Implemented a retrieval-augmented chatbot for enterprise knowledge management using ChromaDB and Hugging Face LLMs.
3. Document Digitization Pipeline - Developed an autonomous AI pipeline for OCR, classification, and extraction of financial/legal documents.
4. Sentiment Analysis Engine - Deployed a multi-lingual sentiment analysis system for customer support and escalation detection.

Career Path & Growth Opportunities

This role provides exposure to the full lifecycle of AI solution development-from research and prototyping to production-grade deployment. The AI Developer can progress to Senior AI Engineer, AI Solution Architect, or Principal AI Scientist, depending on specialization in conversational AI, cloud-native AI infrastructure, or applied generative AI research.