ARTIFICIAL INTELLIGENCE- Job Types
Cloud Computing
VR/AR Developer
Human-Computer Interaction (HCI)
1. Machine Learning Engineer
Role: Build & deploy ML models.
Tools: Python, Scikit-learn, TensorFlow, PyTorch.
Skills: ML algorithms, data preprocessing, model tuning.
2. Data Scientist
Role: Extract insights from data using statistical and ML techniques.
Tools: Python, R, SQL, Jupyter, Tableau.
Skills: Statistics, EDA, machine learning, data storytelling.
3. Data Analyst
Role: Analyze data trends to aid business decisions.
Tools: Excel, SQL, Power BI, Tableau.
Skills: Data wrangling, visualization, basic stats.
4. AI Research Scientist
Role: Develop new AI algorithms and models.
Tools: Python, PyTorch, TensorFlow, MATLAB.
Skills: Deep learning, NLP, reinforcement learning, and academic writing.
5. NLP Engineer
Role: Work on text-based AI, like chatbots or sentiment analysis.
Tools: spaCy, NLTK, Hugging Face, BERT, GPT.
Skills: NLP pipelines, tokenization, embeddings, LLMs.
6. Computer Vision Engineer
Role: Implement AI that processes images/videos.
Tools: OpenCV, PyTorch, TensorFlow, YOLO.
Skills: Image processing, CNNs, object detection.
7. AI Product Manager
Role: Bridge tech and business to build AI products for user.
Tools: JIRA, Figma, Excel, basic ML tools.
Skills: Product lifecycle, AI basics, stakeholder mgmt.
8. Data Engineer
Role: Build data pipelines for ML/data tasks.
Tools: SQL, Apache Spark, Airflow, AWS/GCP.
Skills: ETL, big data, cloud platforms.
9. AI/ML DevOps Engineer (MLOps)
Role: Deploy & monitor ML models in production.
Tools: Docker, Kubernetes, MLflow, AWS/GCP.
Skills: CI/CD, model serving, cloud computing.
10. Robotics Engineer (AI Integration)
Role: Build intelligent systems for automation/robotics.
Tools: ROS, C++, Python, TensorFlow.
Skills: Control systems, AI integration, sensors.
Want this in a table format or downloadable file? Or are you looking for entry-level jobs specifically?
11. Deep Learning Engineer
Role: Design and optimize deep neural networks.
Tools: PyTorch, TensorFlow, Keras.
Skills: CNNs, RNNs, GANs, optimization.
12. AI Ethicist / Responsible AI Specialist
Role: Ensure ethical, fair, and unbiased AI systems.
Tools: Fairlearn, IBM AI Fairness 360, Excel.
Skills: Ethics, bias detection, policy understanding.
13. AI Solutions Architect
Role: Design full-stack AI systems for businesses.
Tools: AWS/GCP, Python, Docker, Kubernetes.
Skills: System architecture, ML lifecycle, cloud infrastructure.
14. Speech Recognition Engineer
Role: Build voice-to-text and voice AI systems.
Tools: Kaldi, DeepSpeech, wav2vec, Whisper.
Skills: Signal processing, audio ML, ASR systems.
15. Reinforcement Learning Engineer
Role: Train AI agents to learn by interacting with environments.
Tools: OpenAI Gym, Stable-Baselines3, PyTorch.
Skills: RL algorithms, reward tuning, environment simulation.
16. Generative AI Engineer
Role: Develop GenAI apps like image/text generation.
Tools: GPT, DALL·E, Stable Diffusion, LangChain.
Skills: Prompt engineering, LLMs, embeddings.
17. AI Business Analyst
Role: Identify AI opportunities in business processes.
Tools: Excel, Power BI, SQL, basic ML tools.
Skills: Business analysis, data insight, AI use cases.
18. Prompt Engineer
Role: Craft prompts for LLMs and fine-tune outputs.
Tools: GPT (OpenAI), Claude, LangChain, Pinecone.
Skills: NLP, creativity, embeddings, vector databases.
19. Chatbot Developer
Role: Build conversational AI systems.
Tools: Rasa, Dialogflow, GPT APIs, Botpress.
Skills: NLP, intents/entities, conversation design.
20. AI Trainer / Data Annotator
Role: Label and curate data for ML model training.
Tools: Labelbox, CVAT, Amazon SageMaker Ground Truth.
Skills: Attention to detail, domain knowledge, basic ML. TOP
- Compiler Engineer
Role: Design, develop, and optimize compilers that translate high-level programming languages into machine-readable code.
- Skills:
- Deep knowledge of compiler theory (parsing, lexing, optimization, code generation)
- Strong programming skills (C, C++, Python, Rust)
- Familiarity with language-specific constructs and runtime environments
- Expertise in algorithms for optimization (e.g., dead code elimination, loop unrolling)
- Understanding of target architecture (x86, ARM, etc.)
- Tools:
- LLVM, GCC (compiler frameworks)
- Flex, Bison (lexical analyzer and parser generators)
- Clang (C/C++/Objective-C compiler)
- GNU Binutils (assembler, linker)
- Python (for scripting and prototyping)
- Compiler Optimization Engineer
Role: Focus on optimizing the generated code for performance improvements, ensuring the compiler produces efficient machine code.
- Skills:
- Deep understanding of performance optimization techniques (loop optimization, memory optimization)
- Knowledge of various compiler optimization passes (constant folding, inlining, dead code elimination)
- Introduction with performance & profiling tools to techniques
- Experience with advanced compiler features (just-in-time compilation, link-time optimization)
- Strong analytical and problem-solving skills
- Tools:
- LLVM/Clang (for optimizing compilers)
- Intel VTune, GNU gprof (profiling tools)
- Valgrind, AddressSanitizer (memory error detection)
- CMake, Ninja (build systems)
- Compiler-based debugging tools (e.g., gdb, lldb)
- Language Developer
Role: Design and implement new programming languages or domain-specific languages (DSLs) by creating compilers or interpreters.
- Skills:
- Expertise in designing language syntax and semantics
- Proficiency in creating parsers, lexers, and interpreters
- Language theory knowledge (context-free grammars, regular expressions)
- Familiarity with existing language compilers and runtime systems
- Experience in developing domain-specific language features
- Tools:
- ANTLR (for creating parsers)
- Flex/Bison (lexical analyzers and parsers)
- LLVM (for creating compilers)
- Racket, LISP (for building DSLs)
- Eclipse IDE, Visual Studio Code (for language development environments)
- Embedded Compiler Developer
Role: Develop compilers tailored for embedded systems with specific constraints (e.g., memory, processing power) and custom hardware.
- Skills:
- Knowledge of embedded systems and architectures (e.g., ARM, MIPS)
- Experience with low-level programming languages (C, assembly)
- Optimization for embedded systems (memory and performance constraints)
- Understanding of cross-compiling techniques
- Familiarity with hardware-specific optimizations
- Tools:
- GCC, Clang (for embedded systems)
- ARM Compiler (for ARM-based systems)
- Keil uVision, MPLAB X (embedded IDEs)
- OpenOCD (for debugging embedded systems)
- QEMU (for emulator-based testing)
- Interpreter Developer
Role: Design and implement interpreters for high-level programming languages or domain-specific languages, focusing on direct execution of code.
- Skills:
- Strong understanding of interpretation vs. compilation
- Proficiency for designing abstract syntax trees (AST)
- Experience with runtime environments and garbage collection
- Knowledge of memory management and virtual machines
- Familiarity with various language execution models (e.g., stack-based, register-based)
- Tools:
- LLVM (for building interpreters with JIT compilation)
- Python, Ruby (for scripting interpreters)
- ANTLR, Bison (for parser generation)
- VM tools like the JVM or V8 JavaScript engine
- Debuggers and profilers (e.g., gdb, Valgrind)
- Static Analysis Engineer
Role: Develop tools to analyze the correctness and efficiency of code without executing it, focusing on compiling and analyzing source code for errors or inefficiencies.
- Skills:
- Strong understanding of static analysis techniques (control flow analysis, data flow analysis)
- Expertise in building static analysis tools and frameworks
- Understanding of compiler internals and static analysis through abstract interpretation.
- Experience with bug-finding tools, such as linters
- Familiarity with security vulnerabilities and memory leak detection
- Tools:
- Clang Static Analyzer
- Coverity, SonarQube (for static code analysis)
- LLVM (for building custom analysis passes)
- PyLint, ESLint (for static analysis in specific languages)
- Klee (for symbolic execution-based analysis)
- Compiler Testing Engineer
Role: Develop and execute test plans to ensure the correctness and performance of compilers, including compiling real-world applications and benchmarking the generated code.
- Skills:
- Knowledge of compiler internals and behavior
- Experience in writing test cases and testing strategies for compilers
- Familiarity with benchmarking tools and techniques
- Proficient in troubleshooting compiler-related problems during development.
- Understanding of software testing methodologies (unit testing, integration testing)
- Tools:
- Google Test, Catch2 (unit testing frameworks)
- LLVM’s testing framework
- Valgrind, ASAN (address sanitizer)
- Benchmarking tools like GNU time
- CI/CD tools (Jenkins, GitLab CI)
- Compiler Documentation Specialist
Role: Write clear, comprehensive documentation for compilers, including user guides, API documentation, and developer manuals for building and optimizing compilers.
- Skills:
- Excellent writing and communication skills
- Ability to understand and explain complex technical concepts
- Experience with software documentation tools and formats (e.g., Markdown, LaTeX)
- Familiarity with compiler design and programming language theory
- Strong attention to detail and organization
- Tools:
- Doxygen (for generating documentation from source code)
- Markdown, LaTeX (for creating documentation)
- Javadoc (for Java-related compiler documentation)
- Sphinx (for Python-based documentation)
- AsciiDoc (for text-based documentation)
- Compiler/Language Researcher
Role: Research and develop novel compiler techniques, optimization algorithms, or new language constructs that improve performance or enable new features in compilers.
- Skills:
- Strong theoretical background in compiler theory, algorithms, and optimization techniques
- Familiarity with advanced topics such as Just-in-Time (JIT) compilation and parallelism
- Research experience in areas like code generation, type inference, and garbage collection
- Ability to conduct experiments and publish findings
- Tools:
- LLVM (for research compilers and tools)
- Formal methods tools (e.g., Coq, Isabelle)
- Research papers and conferences (e.g., ACM SIGPLAN, PLDI)
- Simulation environments for experimental compilers
- MATLAB, R (for data analysis in research)
- Cross-Platform Compiler Developer
Role: Develop compilers or tools that allow code to be compiled and run across multiple platforms or architectures, ensuring compatibility and optimization.
- Skills:
- Knowledge of cross-compiling techniques
- Experience in platform-specific optimizations and porting compilers
- Proficiency in languages such as C/C++ and assembly for different architectures
- Understanding of system architectures (e.g., x86, ARM)
Familiar with Docker for building portable and platform-independent applications.
- Tools:
- GCC, Clang (for cross-compilation)
- CMake, Autotools (for platform-independent builds)
- QEMU (for emulation)
- Docker (for containerized environments) TOP
Companies
AI Research & Model Developers
OpenAI – ChatGPT, Codex, DALL·E
Anthropic – Claude AI
Google DeepMind – Gemini, AlphaFold
Meta AI – LLaMA models
Microsoft Research – Azure OpenAI integration
xAI – Elon Musk’s AI company (Grok)
Big Tech (AI-Powered Products & Platforms)
Google – Bard/Gemini, Vertex AI
Microsoft – Copilot, Azure AI
Amazon (AWS) – Bedrock, SageMaker
Apple – On-device AI, Siri evolution
NVIDIA – Generative AI for graphics, LLM infra
IBM – Watsonx, enterprise GenAI
AI kya he.What are AI & fresher jobs in bangalore 2025.
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