Key Responsibilities:

• Model Development & Integration
  • Design and implement ML/AI models for NLP, speech recognition, recommendation systems, and predictive analytics.
  • Fine-tune large language models (LLMs) and integrate with APIs (e.g., OpenAI, Anthropic, HuggingFace).
  • Build and maintain RAG pipelines with vector databases.
• AI System Engineering
  • Develop APIs and microservices (Python, FastAPI, Flask) to serve AI models.
  • Implement conversation memory, context handling, and multi-turn dialogue.
  • Optimize models for latency, cost-efficiency, and scalability.
• Data Engineering & Processing
  • Build pipelines for data ingestion, cleaning, labeling, and transformation.
  • Manage embeddings, knowledge bases, and structured/unstructured datasets.
  • Conduct feature engineering and dataset preparation for supervised/unsupervised learning.
• Deployment & Monitoring
  • Containerize AI services (Docker, Kubernetes) and deploy to cloud environments (AWS/GCP/Azure).
  • Monitor model performance, drift, and accuracy with continuous retraining workflows.
  • Collaborate with DevOps for CI/CD and model lifecycle management (MLOps).
• Collaboration
  • Work closely with product managers, designers, and full-stack engineers to integrate AI features into products.
  • Participate in code reviews, architecture discussions, and sprint planning.
  • Stay updated with research in AI/ML and recommend new approaches.

Required Skills & Qualifications:

• Strong programming skills in Python and familiarity with ML/AI libraries (TensorFlow, PyTorch, scikit-learn, HuggingFace).
• Experience with LLMs, LangChain, RAG, embeddings, and conversational AI systems.
• Solid understanding of data structures, algorithms, and software engineering principles.
• Hands-on experience with cloud platforms (AWS, GCP, or Azure) for deploying ML solutions.
• Knowledge of databases: PostgreSQL, Redis, Vector DBs (Pinecone, Weaviate, FAISS).
• Experience with APIs, FastAPI/Flask, Docker, and Kubernetes.
• Strong analytical skills and the ability to translate business problems into technical solutions.

Nice to Have:

• Familiarity with speech-to-text (STT), text-to-speech (TTS), and telephony integrations (Twilio, Vonage, etc.).
• Exposure to MLOps tools (MLflow, Kubeflow, SageMaker).
• Contributions to open-source AI/ML projects.
• Experience in SaaS product development.

What We Offer:

• Opportunity to build cutting-edge AI solutions for global clients and SMBs.
• Collaborative and innovation-driven culture.
• Growth opportunities across AI, Cloud, and Cybersecurity practices.
• Flexible work environment and access to partner ecosystems (AWS, GCP, Neysa.ai, etc.).