Job Description – AI EngineerRole Overview
An AI Engineer is responsible for designing, building, deploying, and optimizing AI, Machine Learning, and Generative AI solutions that solve real business problems. This role bridges data, models, and applications, ensuring AI solutions are scalable, reliable, and production‑ready.
AI Engineers work closely with product owners, data engineers, software engineers, and client stakeholders to translate requirements into intelligent systems.
Key Responsibilities
1. AI & Generative AI Development
- Design and build AI and Generative AI solutions using LLMs, NLP, and deep learning models
- Develop applications using OpenAI APIs, Azure OpenAI, HuggingFace, LangChain, Amazon Bedrock, and similar platforms
- Implement Retrieval Augmented Generation (RAG) pipelines using vector databases such as FAISS and Pinecone
- Fine‑tune models using techniques like LoRA and QLoRA
- Build AI‑powered features such as:
- Chatbots and virtual assistants
- Text summarization and extraction
- Question‑answering systems
- Speech‑to‑Text and Text‑to‑Speech solutions
2. Machine Learning & Deep Learning
- Build and deploy ML models using:
- Supervised and unsupervised learning
- Regression and classification algorithms
- Neural networks and ensemble techniques
- Develop deep learning models using TensorFlow, PyTorch, CNNs, RNNs, LSTMs, GANs, BERT and transformer architectures
- Evaluate model performance using metrics such as Perplexity, BLEU, and ROUGE
3. Prompt Engineering
- Design and optimize prompts for:
- Text summarization
- Information extraction
- Question & Answer systems
- Apply advanced prompting techniques such as:
- Few‑shot prompting
- Chain‑of‑Thought (CoT)
- Knowledge‑base grounded prompts
4. Data & Backend Integration
- Work with relational and NoSQL databases:
- MS SQL Server, MySQL, PostgreSQL, MongoDB, Cassandra, HBase
- Build AI services and APIs using Python‑based frameworks
- Integrate AI models with enterprise applications and workflows
- Ensure data quality, security, and compliance in AI pipelines
5. Production & Cloud Readiness
- Deploy AI solutions on cloud platforms (Azure / AWS preferred)
- Implement scalable and secure AI architectures
- Monitor, optimize, and retrain models as required
- Use AI‑assisted development tools such as Microsoft Copilot to accelerate development responsibly
Required Technical Skills
Programming & Frameworks
- Strong proficiency in Python
- NumPy, Pandas, Scikit‑learn, TensorFlow, PyTorch, spaCy, NLTK
- Experience building production‑grade AI pipelines
AI / ML / GenAI
- LLMs and Generative AI
- NLP techniques
- RAG architectures
- Embeddings (Word2Vec, GloVe, ELMo)
- Vector databases
Cloud & Tools
- Azure OpenAI / AWS Bedrock
- HuggingFace ecosystem
- LangChain
- Model fine‑tuning and evaluation tools
Nice‑to‑Have Skills
- Experience with enterprise AI platforms
- Knowledge of MLOps pipelines
- Understanding of AI governance, ethics, and security
- Prior experience in financial services or enterprise domains
Soft Skills & Expectations
- Strong problem‑solving and analytical thinking
- Ability to translate business problems into AI solutions
- Excellent communication with technical and non‑technical stakeholders
- Fast learner with a mindset to adapt to evolving AI technologies
Typical Experience Range
- 3–6 years for mid‑level AI Engineer
- 7+ years for senior / lead AI Engineer roles
(with hands‑on AI/ML and GenAI experience)
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